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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
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| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
| </ul> |
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| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/performance/index.html">Performance</a><ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li> |
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| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.io.html">Input and output</a><ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li> |
| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
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| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
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| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.split.html">mxnet.np.split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.hsplit.html">mxnet.np.hsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.vsplit.html">mxnet.np.vsplit</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.unique.html">mxnet.np.unique</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.flip.html">mxnet.np.flip</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.io.html">Input and output</a><ul> |
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| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.math.html">Mathematical functions</a><ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.cos.html">mxnet.np.cos</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.multiply.html">mxnet.np.multiply</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.true_divide.html">mxnet.np.true_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.remainder.html">mxnet.np.remainder</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.clip.html">mxnet.np.clip</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.cbrt.html">mxnet.np.cbrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.square.html">mxnet.np.square</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.maximum.html">mxnet.np.maximum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.minimum.html">mxnet.np.minimum</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../np/random/index.html">np.random</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.sort.html">Sorting, searching, and counting</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.argmax.html">mxnet.np.argmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.argmin.html">mxnet.np.argmin</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.statistics.html">Statistics</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.min.html">mxnet.np.min</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.max.html">mxnet.np.max</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.mean.html">mxnet.np.mean</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.std.html">mxnet.np.std</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.var.html">mxnet.np.var</a></li> |
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| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../npx/index.html">NPX: NumPy Neural Network Extension</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.set_np.html">mxnet.npx.set_np</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.reset_np.html">mxnet.npx.reset_np</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.cpu.html">mxnet.npx.cpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.cpu_pinned.html">mxnet.npx.cpu_pinned</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.gpu.html">mxnet.npx.gpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.gpu_memory_info.html">mxnet.npx.gpu_memory_info</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.current_context.html">mxnet.npx.current_context</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.num_gpus.html">mxnet.npx.num_gpus</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.activation.html">mxnet.npx.activation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.batch_norm.html">mxnet.npx.batch_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.convolution.html">mxnet.npx.convolution</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.dropout.html">mxnet.npx.dropout</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.embedding.html">mxnet.npx.embedding</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.fully_connected.html">mxnet.npx.fully_connected</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.layer_norm.html">mxnet.npx.layer_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.pooling.html">mxnet.npx.pooling</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.rnn.html">mxnet.npx.rnn</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.leaky_relu.html">mxnet.npx.leaky_relu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.multibox_detection.html">mxnet.npx.multibox_detection</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.multibox_prior.html">mxnet.npx.multibox_prior</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.multibox_target.html">mxnet.npx.multibox_target</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.roi_pooling.html">mxnet.npx.roi_pooling</a></li> |
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| <div class="document"> |
| <div class="page-content" role="main"> |
| |
| <div class="section" id="gluon-model-zoo-vision"> |
| <h1>gluon.model_zoo.vision<a class="headerlink" href="#gluon-model-zoo-vision" title="Permalink to this headline">¶</a></h1> |
| <p>Module for pre-defined neural network models.</p> |
| <p>This module contains definitions for the following model architectures: |
| - <a class="reference external" href="https://arxiv.org/abs/1404.5997">AlexNet</a> |
| - <a class="reference external" href="https://arxiv.org/abs/1608.06993">DenseNet</a> |
| - <a class="reference external" href="http://arxiv.org/abs/1512.00567">Inception V3</a> |
| - <a class="reference external" href="https://arxiv.org/abs/1512.03385">ResNet V1</a> |
| - <a class="reference external" href="https://arxiv.org/abs/1603.05027">ResNet V2</a> |
| - <a class="reference external" href="https://arxiv.org/abs/1602.07360">SqueezeNet</a> |
| - <a class="reference external" href="https://arxiv.org/abs/1409.1556">VGG</a> |
| - <a class="reference external" href="https://arxiv.org/abs/1704.04861">MobileNet</a> |
| - <a class="reference external" href="https://arxiv.org/abs/1801.04381">MobileNetV2</a></p> |
| <p>You can construct a model with random weights by calling its constructor:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">mxnet.gluon.model_zoo</span> <span class="kn">import</span> <span class="n">vision</span> |
| <span class="n">resnet18</span> <span class="o">=</span> <span class="n">vision</span><span class="o">.</span><span class="n">resnet18_v1</span><span class="p">()</span> |
| <span class="n">alexnet</span> <span class="o">=</span> <span class="n">vision</span><span class="o">.</span><span class="n">alexnet</span><span class="p">()</span> |
| <span class="n">squeezenet</span> <span class="o">=</span> <span class="n">vision</span><span class="o">.</span><span class="n">squeezenet1_0</span><span class="p">()</span> |
| <span class="n">densenet</span> <span class="o">=</span> <span class="n">vision</span><span class="o">.</span><span class="n">densenet_161</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| <p>We provide pre-trained models for all the listed models. |
| These models can constructed by passing <code class="docutils literal notranslate"><span class="pre">pretrained=True</span></code>:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">mxnet.gluon.model_zoo</span> <span class="kn">import</span> <span class="n">vision</span> |
| <span class="n">resnet18</span> <span class="o">=</span> <span class="n">vision</span><span class="o">.</span><span class="n">resnet18_v1</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">alexnet</span> <span class="o">=</span> <span class="n">vision</span><span class="o">.</span><span class="n">alexnet</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>All pre-trained models expect input images normalized in the same way, |
| i.e. mini-batches of 3-channel RGB images of shape (N x 3 x H x W), |
| where N is the batch size, and H and W are expected to be at least 224. |
| The images have to be loaded in to a range of [0, 1] and then normalized |
| using <code class="docutils literal notranslate"><span class="pre">mean</span> <span class="pre">=</span> <span class="pre">[0.485,</span> <span class="pre">0.456,</span> <span class="pre">0.406]</span></code> and <code class="docutils literal notranslate"><span class="pre">std</span> <span class="pre">=</span> <span class="pre">[0.229,</span> <span class="pre">0.224,</span> <span class="pre">0.225]</span></code>. |
| The transformation should preferrably happen at preprocessing. You can use |
| <code class="docutils literal notranslate"><span class="pre">mx.image.color_normalize</span></code> for such transformation:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">image</span> <span class="o">=</span> <span class="n">image</span><span class="o">/</span><span class="mi">255</span> |
| <span class="n">normalized</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">color_normalize</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> |
| <span class="n">mean</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">]),</span> |
| <span class="n">std</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">]))</span> |
| </pre></div> |
| </div> |
| <dl class="function"> |
| <dt> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">get_model</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision.html#get_model"><span class="viewcode-link">[source]</span></a></dt> |
| <dd><p>Returns a pre-defined model by name</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of the model.</p></li> |
| <li><p><strong>pretrained</strong> (<em>bool</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>classes</strong> (<em>int</em>) – Number of classes for the output layer.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The model.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../hybrid_block.html#mxnet.gluon.HybridBlock" title="mxnet.gluon.HybridBlock">gluon.HybridBlock</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.get_model" title="mxnet.gluon.model_zoo.vision.get_model"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_model</span></code></a>(name, **kwargs)</p></td> |
| <td><p>Returns a pre-defined model by name</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <div class="section" id="resnet"> |
| <h2>ResNet<a class="headerlink" href="#resnet" title="Permalink to this headline">¶</a></h2> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet18_v1" title="mxnet.gluon.model_zoo.vision.resnet18_v1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resnet18_v1</span></code></a>(**kwargs)</p></td> |
| <td><p>ResNet-18 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet34_v1" title="mxnet.gluon.model_zoo.vision.resnet34_v1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resnet34_v1</span></code></a>(**kwargs)</p></td> |
| <td><p><p>ResNet-34 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet50_v1" title="mxnet.gluon.model_zoo.vision.resnet50_v1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resnet50_v1</span></code></a>(**kwargs)</p></td> |
| <td><p><p>ResNet-50 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet101_v1" title="mxnet.gluon.model_zoo.vision.resnet101_v1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resnet101_v1</span></code></a>(**kwargs)</p></td> |
| <td><p><p>ResNet-101 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet152_v1" title="mxnet.gluon.model_zoo.vision.resnet152_v1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resnet152_v1</span></code></a>(**kwargs)</p></td> |
| <td><p><p>ResNet-152 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet18_v2" title="mxnet.gluon.model_zoo.vision.resnet18_v2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resnet18_v2</span></code></a>(**kwargs)</p></td> |
| <td><p>ResNet-18 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet34_v2" title="mxnet.gluon.model_zoo.vision.resnet34_v2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resnet34_v2</span></code></a>(**kwargs)</p></td> |
| <td><p><p>ResNet-34 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet50_v2" title="mxnet.gluon.model_zoo.vision.resnet50_v2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resnet50_v2</span></code></a>(**kwargs)</p></td> |
| <td><p><p>ResNet-50 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet101_v2" title="mxnet.gluon.model_zoo.vision.resnet101_v2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resnet101_v2</span></code></a>(**kwargs)</p></td> |
| <td><p><p>ResNet-101 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet152_v2" title="mxnet.gluon.model_zoo.vision.resnet152_v2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resnet152_v2</span></code></a>(**kwargs)</p></td> |
| <td><p><p>ResNet-152 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p> |
| </p></td> |
| </tr> |
| </tbody> |
| </table> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.ResNetV1" title="mxnet.gluon.model_zoo.vision.ResNetV1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ResNetV1</span></code></a>(block, layers, channels[, classes, …])</p></td> |
| <td><p><p>ResNet V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.ResNetV2" title="mxnet.gluon.model_zoo.vision.ResNetV2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ResNetV2</span></code></a>(block, layers, channels[, classes, …])</p></td> |
| <td><p><p>ResNet V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.BasicBlockV1" title="mxnet.gluon.model_zoo.vision.BasicBlockV1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BasicBlockV1</span></code></a>(channels, stride[, downsample, …])</p></td> |
| <td><p><p>BasicBlock V1 from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.This is used for ResNet V1 for 18, 34 layers..</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.BasicBlockV2" title="mxnet.gluon.model_zoo.vision.BasicBlockV2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BasicBlockV2</span></code></a>(channels, stride[, downsample, …])</p></td> |
| <td><p><p>BasicBlock V2 from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.This is used for ResNet V2 for 18, 34 layers..</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.BottleneckV1" title="mxnet.gluon.model_zoo.vision.BottleneckV1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BottleneckV1</span></code></a>(channels, stride[, downsample, …])</p></td> |
| <td><p><p>Bottleneck V1 from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.This is used for ResNet V1 for 50, 101, 152 layers..</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.BottleneckV2" title="mxnet.gluon.model_zoo.vision.BottleneckV2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BottleneckV2</span></code></a>(channels, stride[, downsample, …])</p></td> |
| <td><p><p>Bottleneck V2 from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.This is used for ResNet V2 for 50, 101, 152 layers..</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.get_resnet" title="mxnet.gluon.model_zoo.vision.get_resnet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_resnet</span></code></a>(version, num_layers[, …])</p></td> |
| <td><p><p>ResNet V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.ResNet V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper..</p> |
| </p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="id17"> |
| <h2>VGG<a class="headerlink" href="#id17" title="Permalink to this headline">¶</a></h2> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.vgg11" title="mxnet.gluon.model_zoo.vision.vgg11"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vgg11</span></code></a>(**kwargs)</p></td> |
| <td><p>VGG-11 model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.vgg13" title="mxnet.gluon.model_zoo.vision.vgg13"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vgg13</span></code></a>(**kwargs)</p></td> |
| <td><p><p>VGG-13 model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.vgg16" title="mxnet.gluon.model_zoo.vision.vgg16"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vgg16</span></code></a>(**kwargs)</p></td> |
| <td><p><p>VGG-16 model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.vgg19" title="mxnet.gluon.model_zoo.vision.vgg19"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vgg19</span></code></a>(**kwargs)</p></td> |
| <td><p><p>VGG-19 model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.vgg11_bn" title="mxnet.gluon.model_zoo.vision.vgg11_bn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vgg11_bn</span></code></a>(**kwargs)</p></td> |
| <td><p><p>VGG-11 model with batch normalization from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.vgg13_bn" title="mxnet.gluon.model_zoo.vision.vgg13_bn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vgg13_bn</span></code></a>(**kwargs)</p></td> |
| <td><p><p>VGG-13 model with batch normalization from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.vgg16_bn" title="mxnet.gluon.model_zoo.vision.vgg16_bn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vgg16_bn</span></code></a>(**kwargs)</p></td> |
| <td><p><p>VGG-16 model with batch normalization from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.vgg19_bn" title="mxnet.gluon.model_zoo.vision.vgg19_bn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vgg19_bn</span></code></a>(**kwargs)</p></td> |
| <td><p><p>VGG-19 model with batch normalization from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| </tbody> |
| </table> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.VGG" title="mxnet.gluon.model_zoo.vision.VGG"><code class="xref py py-obj docutils literal notranslate"><span class="pre">VGG</span></code></a>(layers, filters[, classes, batch_norm])</p></td> |
| <td><p><p>VGG model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.get_vgg" title="mxnet.gluon.model_zoo.vision.get_vgg"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_vgg</span></code></a>(num_layers[, pretrained, ctx, root])</p></td> |
| <td><p><p>VGG model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="id27"> |
| <h2>Alexnet<a class="headerlink" href="#id27" title="Permalink to this headline">¶</a></h2> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.alexnet" title="mxnet.gluon.model_zoo.vision.alexnet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">alexnet</span></code></a>([pretrained, ctx, root])</p></td> |
| <td><p>AlexNet model from the <a class="reference external" href="https://arxiv.org/abs/1404.5997">“One weird trick…”</a> paper.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.AlexNet" title="mxnet.gluon.model_zoo.vision.AlexNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AlexNet</span></code></a>([classes])</p></td> |
| <td><p><p>AlexNet model from the <a class="reference external" href="https://arxiv.org/abs/1404.5997">“One weird trick…”</a> paper.</p> |
| </p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="id29"> |
| <h2>DenseNet<a class="headerlink" href="#id29" title="Permalink to this headline">¶</a></h2> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.densenet121" title="mxnet.gluon.model_zoo.vision.densenet121"><code class="xref py py-obj docutils literal notranslate"><span class="pre">densenet121</span></code></a>(**kwargs)</p></td> |
| <td><p>Densenet-BC 121-layer model from the <a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.densenet161" title="mxnet.gluon.model_zoo.vision.densenet161"><code class="xref py py-obj docutils literal notranslate"><span class="pre">densenet161</span></code></a>(**kwargs)</p></td> |
| <td><p><p>Densenet-BC 161-layer model from the <a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.densenet169" title="mxnet.gluon.model_zoo.vision.densenet169"><code class="xref py py-obj docutils literal notranslate"><span class="pre">densenet169</span></code></a>(**kwargs)</p></td> |
| <td><p><p>Densenet-BC 169-layer model from the <a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.densenet201" title="mxnet.gluon.model_zoo.vision.densenet201"><code class="xref py py-obj docutils literal notranslate"><span class="pre">densenet201</span></code></a>(**kwargs)</p></td> |
| <td><p><p>Densenet-BC 201-layer model from the <a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</p> |
| </p></td> |
| </tr> |
| </tbody> |
| </table> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.DenseNet" title="mxnet.gluon.model_zoo.vision.DenseNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DenseNet</span></code></a>(num_init_features, growth_rate, …)</p></td> |
| <td><p><p>Densenet-BC model from the <a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</p> |
| </p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="id34"> |
| <h2>SqueezeNet<a class="headerlink" href="#id34" title="Permalink to this headline">¶</a></h2> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.squeezenet1_0" title="mxnet.gluon.model_zoo.vision.squeezenet1_0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">squeezenet1_0</span></code></a>(**kwargs)</p></td> |
| <td><p>SqueezeNet 1.0 model from the <a class="reference external" href="https://arxiv.org/abs/1602.07360">“SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size”</a> paper.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.squeezenet1_1" title="mxnet.gluon.model_zoo.vision.squeezenet1_1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">squeezenet1_1</span></code></a>(**kwargs)</p></td> |
| <td><p>SqueezeNet 1.1 model from the <a class="reference external" href="https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1">official SqueezeNet repo</a>.SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy..</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.SqueezeNet" title="mxnet.gluon.model_zoo.vision.SqueezeNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SqueezeNet</span></code></a>(version[, classes])</p></td> |
| <td><p><p>SqueezeNet model from the <a class="reference external" href="https://arxiv.org/abs/1602.07360">“SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size”</a> paper.SqueezeNet 1.1 model from the <a class="reference external" href="https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1">official SqueezeNet repo</a>.SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy..</p> |
| </p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="inception"> |
| <h2>Inception<a class="headerlink" href="#inception" title="Permalink to this headline">¶</a></h2> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.inception_v3" title="mxnet.gluon.model_zoo.vision.inception_v3"><code class="xref py py-obj docutils literal notranslate"><span class="pre">inception_v3</span></code></a>([pretrained, ctx, root])</p></td> |
| <td><p>Inception v3 model from <a class="reference external" href="http://arxiv.org/abs/1512.00567">“Rethinking the Inception Architecture for Computer Vision”</a> paper.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.Inception3" title="mxnet.gluon.model_zoo.vision.Inception3"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Inception3</span></code></a>([classes])</p></td> |
| <td><p><p>Inception v3 model from <a class="reference external" href="http://arxiv.org/abs/1512.00567">“Rethinking the Inception Architecture for Computer Vision”</a> paper.</p> |
| </p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="id38"> |
| <h2>MobileNet<a class="headerlink" href="#id38" title="Permalink to this headline">¶</a></h2> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.mobilenet1_0" title="mxnet.gluon.model_zoo.vision.mobilenet1_0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mobilenet1_0</span></code></a>(**kwargs)</p></td> |
| <td><p>MobileNet model from the <a class="reference external" href="https://arxiv.org/abs/1704.04861">“MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”</a> paper, with width multiplier 1.0.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.mobilenet0_75" title="mxnet.gluon.model_zoo.vision.mobilenet0_75"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mobilenet0_75</span></code></a>(**kwargs)</p></td> |
| <td><p><p>MobileNet model from the <a class="reference external" href="https://arxiv.org/abs/1704.04861">“MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”</a> paper, with width multiplier 0.75.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.mobilenet0_5" title="mxnet.gluon.model_zoo.vision.mobilenet0_5"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mobilenet0_5</span></code></a>(**kwargs)</p></td> |
| <td><p><p>MobileNet model from the <a class="reference external" href="https://arxiv.org/abs/1704.04861">“MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”</a> paper, with width multiplier 0.5.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.mobilenet0_25" title="mxnet.gluon.model_zoo.vision.mobilenet0_25"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mobilenet0_25</span></code></a>(**kwargs)</p></td> |
| <td><p><p>MobileNet model from the <a class="reference external" href="https://arxiv.org/abs/1704.04861">“MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”</a> paper, with width multiplier 0.25.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.mobilenet_v2_1_0" title="mxnet.gluon.model_zoo.vision.mobilenet_v2_1_0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mobilenet_v2_1_0</span></code></a>(**kwargs)</p></td> |
| <td><p>MobileNetV2 model from the <a class="reference external" href="https://arxiv.org/abs/1801.04381">“Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation”</a> paper.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.mobilenet_v2_0_75" title="mxnet.gluon.model_zoo.vision.mobilenet_v2_0_75"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mobilenet_v2_0_75</span></code></a>(**kwargs)</p></td> |
| <td><p><p>MobileNetV2 model from the <a class="reference external" href="https://arxiv.org/abs/1801.04381">“Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.mobilenet_v2_0_5" title="mxnet.gluon.model_zoo.vision.mobilenet_v2_0_5"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mobilenet_v2_0_5</span></code></a>(**kwargs)</p></td> |
| <td><p><p>MobileNetV2 model from the <a class="reference external" href="https://arxiv.org/abs/1801.04381">“Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.mobilenet_v2_0_25" title="mxnet.gluon.model_zoo.vision.mobilenet_v2_0_25"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mobilenet_v2_0_25</span></code></a>(**kwargs)</p></td> |
| <td><p><p>MobileNetV2 model from the <a class="reference external" href="https://arxiv.org/abs/1801.04381">“Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation”</a> paper.</p> |
| </p></td> |
| </tr> |
| </tbody> |
| </table> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.MobileNet" title="mxnet.gluon.model_zoo.vision.MobileNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MobileNet</span></code></a>([multiplier, classes])</p></td> |
| <td><p><p>MobileNet model from the <a class="reference external" href="https://arxiv.org/abs/1704.04861">“MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.MobileNetV2" title="mxnet.gluon.model_zoo.vision.MobileNetV2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MobileNetV2</span></code></a>([multiplier, classes])</p></td> |
| <td><p><p>MobileNetV2 model from the <a class="reference external" href="https://arxiv.org/abs/1801.04381">“Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation”</a> paper.</p> |
| </p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="module-mxnet.gluon.model_zoo.vision"> |
| <span id="api-reference"></span><h2>API Reference<a class="headerlink" href="#module-mxnet.gluon.model_zoo.vision" title="Permalink to this headline">¶</a></h2> |
| <p>Module for pre-defined neural network models.</p> |
| <p>This module contains definitions for the following model architectures: |
| - <a class="reference external" href="https://arxiv.org/abs/1404.5997">AlexNet</a> |
| - <a class="reference external" href="https://arxiv.org/abs/1608.06993">DenseNet</a> |
| - <a class="reference external" href="http://arxiv.org/abs/1512.00567">Inception V3</a> |
| - <a class="reference external" href="https://arxiv.org/abs/1512.03385">ResNet V1</a> |
| - <a class="reference external" href="https://arxiv.org/abs/1603.05027">ResNet V2</a> |
| - <a class="reference external" href="https://arxiv.org/abs/1602.07360">SqueezeNet</a> |
| - <a class="reference external" href="https://arxiv.org/abs/1409.1556">VGG</a> |
| - <a class="reference external" href="https://arxiv.org/abs/1704.04861">MobileNet</a> |
| - <a class="reference external" href="https://arxiv.org/abs/1801.04381">MobileNetV2</a></p> |
| <p>You can construct a model with random weights by calling its constructor:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">mxnet.gluon.model_zoo</span> <span class="kn">import</span> <span class="n">vision</span> |
| <span class="n">resnet18</span> <span class="o">=</span> <span class="n">vision</span><span class="o">.</span><span class="n">resnet18_v1</span><span class="p">()</span> |
| <span class="n">alexnet</span> <span class="o">=</span> <span class="n">vision</span><span class="o">.</span><span class="n">alexnet</span><span class="p">()</span> |
| <span class="n">squeezenet</span> <span class="o">=</span> <span class="n">vision</span><span class="o">.</span><span class="n">squeezenet1_0</span><span class="p">()</span> |
| <span class="n">densenet</span> <span class="o">=</span> <span class="n">vision</span><span class="o">.</span><span class="n">densenet_161</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| <p>We provide pre-trained models for all the listed models. |
| These models can constructed by passing <code class="docutils literal notranslate"><span class="pre">pretrained=True</span></code>:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">mxnet.gluon.model_zoo</span> <span class="kn">import</span> <span class="n">vision</span> |
| <span class="n">resnet18</span> <span class="o">=</span> <span class="n">vision</span><span class="o">.</span><span class="n">resnet18_v1</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">alexnet</span> <span class="o">=</span> <span class="n">vision</span><span class="o">.</span><span class="n">alexnet</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>All pre-trained models expect input images normalized in the same way, |
| i.e. mini-batches of 3-channel RGB images of shape (N x 3 x H x W), |
| where N is the batch size, and H and W are expected to be at least 224. |
| The images have to be loaded in to a range of [0, 1] and then normalized |
| using <code class="docutils literal notranslate"><span class="pre">mean</span> <span class="pre">=</span> <span class="pre">[0.485,</span> <span class="pre">0.456,</span> <span class="pre">0.406]</span></code> and <code class="docutils literal notranslate"><span class="pre">std</span> <span class="pre">=</span> <span class="pre">[0.229,</span> <span class="pre">0.224,</span> <span class="pre">0.225]</span></code>. |
| The transformation should preferrably happen at preprocessing. You can use |
| <code class="docutils literal notranslate"><span class="pre">mx.image.color_normalize</span></code> for such transformation:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">image</span> <span class="o">=</span> <span class="n">image</span><span class="o">/</span><span class="mi">255</span> |
| <span class="n">normalized</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">color_normalize</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> |
| <span class="n">mean</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">]),</span> |
| <span class="n">std</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">]))</span> |
| </pre></div> |
| </div> |
| <p><strong>Classes</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.AlexNet" title="mxnet.gluon.model_zoo.vision.AlexNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AlexNet</span></code></a>([classes])</p></td> |
| <td><p><p>AlexNet model from the <a class="reference external" href="https://arxiv.org/abs/1404.5997">“One weird trick…”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.BasicBlockV1" title="mxnet.gluon.model_zoo.vision.BasicBlockV1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BasicBlockV1</span></code></a>(channels, stride[, downsample, …])</p></td> |
| <td><p><p>BasicBlock V1 from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.BasicBlockV2" title="mxnet.gluon.model_zoo.vision.BasicBlockV2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BasicBlockV2</span></code></a>(channels, stride[, downsample, …])</p></td> |
| <td><p><p>BasicBlock V2 from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.BottleneckV1" title="mxnet.gluon.model_zoo.vision.BottleneckV1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BottleneckV1</span></code></a>(channels, stride[, downsample, …])</p></td> |
| <td><p><p>Bottleneck V1 from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.BottleneckV2" title="mxnet.gluon.model_zoo.vision.BottleneckV2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BottleneckV2</span></code></a>(channels, stride[, downsample, …])</p></td> |
| <td><p><p>Bottleneck V2 from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.DenseNet" title="mxnet.gluon.model_zoo.vision.DenseNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DenseNet</span></code></a>(num_init_features, growth_rate, …)</p></td> |
| <td><p><p>Densenet-BC model from the <a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.Inception3" title="mxnet.gluon.model_zoo.vision.Inception3"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Inception3</span></code></a>([classes])</p></td> |
| <td><p><p>Inception v3 model from <a class="reference external" href="http://arxiv.org/abs/1512.00567">“Rethinking the Inception Architecture for Computer Vision”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.MobileNet" title="mxnet.gluon.model_zoo.vision.MobileNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MobileNet</span></code></a>([multiplier, classes])</p></td> |
| <td><p><p>MobileNet model from the <a class="reference external" href="https://arxiv.org/abs/1704.04861">“MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.MobileNetV2" title="mxnet.gluon.model_zoo.vision.MobileNetV2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MobileNetV2</span></code></a>([multiplier, classes])</p></td> |
| <td><p><p>MobileNetV2 model from the <a class="reference external" href="https://arxiv.org/abs/1801.04381">“Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.ResNetV1" title="mxnet.gluon.model_zoo.vision.ResNetV1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ResNetV1</span></code></a>(block, layers, channels[, classes, …])</p></td> |
| <td><p><p>ResNet V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.ResNetV2" title="mxnet.gluon.model_zoo.vision.ResNetV2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ResNetV2</span></code></a>(block, layers, channels[, classes, …])</p></td> |
| <td><p><p>ResNet V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.SqueezeNet" title="mxnet.gluon.model_zoo.vision.SqueezeNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SqueezeNet</span></code></a>(version[, classes])</p></td> |
| <td><p><p>SqueezeNet model from the <a class="reference external" href="https://arxiv.org/abs/1602.07360">“SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.VGG" title="mxnet.gluon.model_zoo.vision.VGG"><code class="xref py py-obj docutils literal notranslate"><span class="pre">VGG</span></code></a>(layers, filters[, classes, batch_norm])</p></td> |
| <td><p><p>VGG model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Functions</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.alexnet" title="mxnet.gluon.model_zoo.vision.alexnet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">alexnet</span></code></a>([pretrained, ctx, root])</p></td> |
| <td><p><p>AlexNet model from the <a class="reference external" href="https://arxiv.org/abs/1404.5997">“One weird trick…”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.densenet121" title="mxnet.gluon.model_zoo.vision.densenet121"><code class="xref py py-obj docutils literal notranslate"><span class="pre">densenet121</span></code></a>(**kwargs)</p></td> |
| <td><p><p>Densenet-BC 121-layer model from the <a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.densenet161" title="mxnet.gluon.model_zoo.vision.densenet161"><code class="xref py py-obj docutils literal notranslate"><span class="pre">densenet161</span></code></a>(**kwargs)</p></td> |
| <td><p><p>Densenet-BC 161-layer model from the <a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.densenet169" title="mxnet.gluon.model_zoo.vision.densenet169"><code class="xref py py-obj docutils literal notranslate"><span class="pre">densenet169</span></code></a>(**kwargs)</p></td> |
| <td><p><p>Densenet-BC 169-layer model from the <a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.densenet201" title="mxnet.gluon.model_zoo.vision.densenet201"><code class="xref py py-obj docutils literal notranslate"><span class="pre">densenet201</span></code></a>(**kwargs)</p></td> |
| <td><p><p>Densenet-BC 201-layer model from the <a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.get_mobilenet" title="mxnet.gluon.model_zoo.vision.get_mobilenet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_mobilenet</span></code></a>(multiplier[, pretrained, ctx, …])</p></td> |
| <td><p><p>MobileNet model from the <a class="reference external" href="https://arxiv.org/abs/1704.04861">“MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.get_mobilenet_v2" title="mxnet.gluon.model_zoo.vision.get_mobilenet_v2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_mobilenet_v2</span></code></a>(multiplier[, pretrained, …])</p></td> |
| <td><p><p>MobileNetV2 model from the <a class="reference external" href="https://arxiv.org/abs/1801.04381">“Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.get_model" title="mxnet.gluon.model_zoo.vision.get_model"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_model</span></code></a>(name, **kwargs)</p></td> |
| <td><p>Returns a pre-defined model by name</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.get_resnet" title="mxnet.gluon.model_zoo.vision.get_resnet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_resnet</span></code></a>(version, num_layers[, …])</p></td> |
| <td><p><p>ResNet V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.get_vgg" title="mxnet.gluon.model_zoo.vision.get_vgg"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_vgg</span></code></a>(num_layers[, pretrained, ctx, root])</p></td> |
| <td><p><p>VGG model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.inception_v3" title="mxnet.gluon.model_zoo.vision.inception_v3"><code class="xref py py-obj docutils literal notranslate"><span class="pre">inception_v3</span></code></a>([pretrained, ctx, root])</p></td> |
| <td><p><p>Inception v3 model from <a class="reference external" href="http://arxiv.org/abs/1512.00567">“Rethinking the Inception Architecture for Computer Vision”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.mobilenet0_25" title="mxnet.gluon.model_zoo.vision.mobilenet0_25"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mobilenet0_25</span></code></a>(**kwargs)</p></td> |
| <td><p><p>MobileNet model from the <a class="reference external" href="https://arxiv.org/abs/1704.04861">“MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”</a> paper, with width multiplier 0.25.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.mobilenet0_5" title="mxnet.gluon.model_zoo.vision.mobilenet0_5"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mobilenet0_5</span></code></a>(**kwargs)</p></td> |
| <td><p><p>MobileNet model from the <a class="reference external" href="https://arxiv.org/abs/1704.04861">“MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”</a> paper, with width multiplier 0.5.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.mobilenet0_75" title="mxnet.gluon.model_zoo.vision.mobilenet0_75"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mobilenet0_75</span></code></a>(**kwargs)</p></td> |
| <td><p><p>MobileNet model from the <a class="reference external" href="https://arxiv.org/abs/1704.04861">“MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”</a> paper, with width multiplier 0.75.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.mobilenet1_0" title="mxnet.gluon.model_zoo.vision.mobilenet1_0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mobilenet1_0</span></code></a>(**kwargs)</p></td> |
| <td><p><p>MobileNet model from the <a class="reference external" href="https://arxiv.org/abs/1704.04861">“MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”</a> paper, with width multiplier 1.0.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.mobilenet_v2_0_25" title="mxnet.gluon.model_zoo.vision.mobilenet_v2_0_25"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mobilenet_v2_0_25</span></code></a>(**kwargs)</p></td> |
| <td><p><p>MobileNetV2 model from the <a class="reference external" href="https://arxiv.org/abs/1801.04381">“Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.mobilenet_v2_0_5" title="mxnet.gluon.model_zoo.vision.mobilenet_v2_0_5"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mobilenet_v2_0_5</span></code></a>(**kwargs)</p></td> |
| <td><p><p>MobileNetV2 model from the <a class="reference external" href="https://arxiv.org/abs/1801.04381">“Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.mobilenet_v2_0_75" title="mxnet.gluon.model_zoo.vision.mobilenet_v2_0_75"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mobilenet_v2_0_75</span></code></a>(**kwargs)</p></td> |
| <td><p><p>MobileNetV2 model from the <a class="reference external" href="https://arxiv.org/abs/1801.04381">“Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.mobilenet_v2_1_0" title="mxnet.gluon.model_zoo.vision.mobilenet_v2_1_0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mobilenet_v2_1_0</span></code></a>(**kwargs)</p></td> |
| <td><p><p>MobileNetV2 model from the <a class="reference external" href="https://arxiv.org/abs/1801.04381">“Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet101_v1" title="mxnet.gluon.model_zoo.vision.resnet101_v1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resnet101_v1</span></code></a>(**kwargs)</p></td> |
| <td><p><p>ResNet-101 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet101_v2" title="mxnet.gluon.model_zoo.vision.resnet101_v2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resnet101_v2</span></code></a>(**kwargs)</p></td> |
| <td><p><p>ResNet-101 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet152_v1" title="mxnet.gluon.model_zoo.vision.resnet152_v1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resnet152_v1</span></code></a>(**kwargs)</p></td> |
| <td><p><p>ResNet-152 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet152_v2" title="mxnet.gluon.model_zoo.vision.resnet152_v2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resnet152_v2</span></code></a>(**kwargs)</p></td> |
| <td><p><p>ResNet-152 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet18_v1" title="mxnet.gluon.model_zoo.vision.resnet18_v1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resnet18_v1</span></code></a>(**kwargs)</p></td> |
| <td><p><p>ResNet-18 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet18_v2" title="mxnet.gluon.model_zoo.vision.resnet18_v2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resnet18_v2</span></code></a>(**kwargs)</p></td> |
| <td><p><p>ResNet-18 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet34_v1" title="mxnet.gluon.model_zoo.vision.resnet34_v1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resnet34_v1</span></code></a>(**kwargs)</p></td> |
| <td><p><p>ResNet-34 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet34_v2" title="mxnet.gluon.model_zoo.vision.resnet34_v2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resnet34_v2</span></code></a>(**kwargs)</p></td> |
| <td><p><p>ResNet-34 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet50_v1" title="mxnet.gluon.model_zoo.vision.resnet50_v1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resnet50_v1</span></code></a>(**kwargs)</p></td> |
| <td><p><p>ResNet-50 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.resnet50_v2" title="mxnet.gluon.model_zoo.vision.resnet50_v2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resnet50_v2</span></code></a>(**kwargs)</p></td> |
| <td><p><p>ResNet-50 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.squeezenet1_0" title="mxnet.gluon.model_zoo.vision.squeezenet1_0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">squeezenet1_0</span></code></a>(**kwargs)</p></td> |
| <td><p><p>SqueezeNet 1.0 model from the <a class="reference external" href="https://arxiv.org/abs/1602.07360">“SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.squeezenet1_1" title="mxnet.gluon.model_zoo.vision.squeezenet1_1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">squeezenet1_1</span></code></a>(**kwargs)</p></td> |
| <td><p><p>SqueezeNet 1.1 model from the <a class="reference external" href="https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1">official SqueezeNet repo</a>.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.vgg11" title="mxnet.gluon.model_zoo.vision.vgg11"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vgg11</span></code></a>(**kwargs)</p></td> |
| <td><p><p>VGG-11 model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.vgg11_bn" title="mxnet.gluon.model_zoo.vision.vgg11_bn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vgg11_bn</span></code></a>(**kwargs)</p></td> |
| <td><p><p>VGG-11 model with batch normalization from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.vgg13" title="mxnet.gluon.model_zoo.vision.vgg13"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vgg13</span></code></a>(**kwargs)</p></td> |
| <td><p><p>VGG-13 model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.vgg13_bn" title="mxnet.gluon.model_zoo.vision.vgg13_bn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vgg13_bn</span></code></a>(**kwargs)</p></td> |
| <td><p><p>VGG-13 model with batch normalization from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.vgg16" title="mxnet.gluon.model_zoo.vision.vgg16"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vgg16</span></code></a>(**kwargs)</p></td> |
| <td><p><p>VGG-16 model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.vgg16_bn" title="mxnet.gluon.model_zoo.vision.vgg16_bn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vgg16_bn</span></code></a>(**kwargs)</p></td> |
| <td><p><p>VGG-16 model with batch normalization from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.vgg19" title="mxnet.gluon.model_zoo.vision.vgg19"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vgg19</span></code></a>(**kwargs)</p></td> |
| <td><p><p>VGG-19 model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.vgg19_bn" title="mxnet.gluon.model_zoo.vision.vgg19_bn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vgg19_bn</span></code></a>(**kwargs)</p></td> |
| <td><p><p>VGG-19 model with batch normalization from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| </p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="class"> |
| <dt id="mxnet.gluon.model_zoo.vision.AlexNet"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">AlexNet</code><span class="sig-paren">(</span><em class="sig-param">classes=1000</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/alexnet.html#AlexNet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.AlexNet" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.block.HybridBlock</span></code></p> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.AlexNet.hybrid_forward" title="mxnet.gluon.model_zoo.vision.AlexNet.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, x)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>AlexNet model from the <a class="reference external" href="https://arxiv.org/abs/1404.5997">“One weird trick…”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>classes</strong> (<em>int</em><em>, </em><em>default 1000</em>) – Number of classes for the output layer.</p> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.model_zoo.vision.AlexNet.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">x</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/alexnet.html#AlexNet.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.AlexNet.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.model_zoo.vision.BasicBlockV1"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">BasicBlockV1</code><span class="sig-paren">(</span><em class="sig-param">channels</em>, <em class="sig-param">stride</em>, <em class="sig-param">downsample=False</em>, <em class="sig-param">in_channels=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/resnet.html#BasicBlockV1"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.BasicBlockV1" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.block.HybridBlock</span></code></p> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.BasicBlockV1.hybrid_forward" title="mxnet.gluon.model_zoo.vision.BasicBlockV1.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, x)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>BasicBlock V1 from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper. |
| This is used for ResNet V1 for 18, 34 layers.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>channels</strong> (<em>int</em>) – Number of output channels.</p></li> |
| <li><p><strong>stride</strong> (<em>int</em>) – Stride size.</p></li> |
| <li><p><strong>downsample</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to downsample the input.</p></li> |
| <li><p><strong>in_channels</strong> (<em>int</em><em>, </em><em>default 0</em>) – Number of input channels. Default is 0, to infer from the graph.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.model_zoo.vision.BasicBlockV1.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">x</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/resnet.html#BasicBlockV1.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.BasicBlockV1.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.model_zoo.vision.BasicBlockV2"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">BasicBlockV2</code><span class="sig-paren">(</span><em class="sig-param">channels</em>, <em class="sig-param">stride</em>, <em class="sig-param">downsample=False</em>, <em class="sig-param">in_channels=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/resnet.html#BasicBlockV2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.BasicBlockV2" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.block.HybridBlock</span></code></p> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.BasicBlockV2.hybrid_forward" title="mxnet.gluon.model_zoo.vision.BasicBlockV2.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, x)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>BasicBlock V2 from |
| <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper. |
| This is used for ResNet V2 for 18, 34 layers.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>channels</strong> (<em>int</em>) – Number of output channels.</p></li> |
| <li><p><strong>stride</strong> (<em>int</em>) – Stride size.</p></li> |
| <li><p><strong>downsample</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to downsample the input.</p></li> |
| <li><p><strong>in_channels</strong> (<em>int</em><em>, </em><em>default 0</em>) – Number of input channels. Default is 0, to infer from the graph.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.model_zoo.vision.BasicBlockV2.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">x</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/resnet.html#BasicBlockV2.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.BasicBlockV2.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.model_zoo.vision.BottleneckV1"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">BottleneckV1</code><span class="sig-paren">(</span><em class="sig-param">channels</em>, <em class="sig-param">stride</em>, <em class="sig-param">downsample=False</em>, <em class="sig-param">in_channels=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/resnet.html#BottleneckV1"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.BottleneckV1" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.block.HybridBlock</span></code></p> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.BottleneckV1.hybrid_forward" title="mxnet.gluon.model_zoo.vision.BottleneckV1.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, x)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>Bottleneck V1 from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper. |
| This is used for ResNet V1 for 50, 101, 152 layers.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>channels</strong> (<em>int</em>) – Number of output channels.</p></li> |
| <li><p><strong>stride</strong> (<em>int</em>) – Stride size.</p></li> |
| <li><p><strong>downsample</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to downsample the input.</p></li> |
| <li><p><strong>in_channels</strong> (<em>int</em><em>, </em><em>default 0</em>) – Number of input channels. Default is 0, to infer from the graph.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.model_zoo.vision.BottleneckV1.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">x</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/resnet.html#BottleneckV1.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.BottleneckV1.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.model_zoo.vision.BottleneckV2"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">BottleneckV2</code><span class="sig-paren">(</span><em class="sig-param">channels</em>, <em class="sig-param">stride</em>, <em class="sig-param">downsample=False</em>, <em class="sig-param">in_channels=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/resnet.html#BottleneckV2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.BottleneckV2" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.block.HybridBlock</span></code></p> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.BottleneckV2.hybrid_forward" title="mxnet.gluon.model_zoo.vision.BottleneckV2.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, x)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>Bottleneck V2 from |
| <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper. |
| This is used for ResNet V2 for 50, 101, 152 layers.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>channels</strong> (<em>int</em>) – Number of output channels.</p></li> |
| <li><p><strong>stride</strong> (<em>int</em>) – Stride size.</p></li> |
| <li><p><strong>downsample</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to downsample the input.</p></li> |
| <li><p><strong>in_channels</strong> (<em>int</em><em>, </em><em>default 0</em>) – Number of input channels. Default is 0, to infer from the graph.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.model_zoo.vision.BottleneckV2.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">x</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/resnet.html#BottleneckV2.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.BottleneckV2.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.model_zoo.vision.DenseNet"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">DenseNet</code><span class="sig-paren">(</span><em class="sig-param">num_init_features</em>, <em class="sig-param">growth_rate</em>, <em class="sig-param">block_config</em>, <em class="sig-param">bn_size=4</em>, <em class="sig-param">dropout=0</em>, <em class="sig-param">classes=1000</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/densenet.html#DenseNet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.DenseNet" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.block.HybridBlock</span></code></p> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.DenseNet.hybrid_forward" title="mxnet.gluon.model_zoo.vision.DenseNet.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, x)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>Densenet-BC model from the |
| <a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>num_init_features</strong> (<em>int</em>) – Number of filters to learn in the first convolution layer.</p></li> |
| <li><p><strong>growth_rate</strong> (<em>int</em>) – Number of filters to add each layer (<cite>k</cite> in the paper).</p></li> |
| <li><p><strong>block_config</strong> (<em>list of int</em>) – List of integers for numbers of layers in each pooling block.</p></li> |
| <li><p><strong>bn_size</strong> (<em>int</em><em>, </em><em>default 4</em>) – Multiplicative factor for number of bottle neck layers. |
| (i.e. bn_size * k features in the bottleneck layer)</p></li> |
| <li><p><strong>dropout</strong> (<em>float</em><em>, </em><em>default 0</em>) – Rate of dropout after each dense layer.</p></li> |
| <li><p><strong>classes</strong> (<em>int</em><em>, </em><em>default 1000</em>) – Number of classification classes.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.model_zoo.vision.DenseNet.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">x</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/densenet.html#DenseNet.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.DenseNet.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.model_zoo.vision.Inception3"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">Inception3</code><span class="sig-paren">(</span><em class="sig-param">classes=1000</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/inception.html#Inception3"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.Inception3" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.block.HybridBlock</span></code></p> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.Inception3.hybrid_forward" title="mxnet.gluon.model_zoo.vision.Inception3.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, x)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>Inception v3 model from |
| <a class="reference external" href="http://arxiv.org/abs/1512.00567">“Rethinking the Inception Architecture for Computer Vision”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>classes</strong> (<em>int</em><em>, </em><em>default 1000</em>) – Number of classification classes.</p> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.model_zoo.vision.Inception3.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">x</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/inception.html#Inception3.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.Inception3.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.model_zoo.vision.MobileNet"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">MobileNet</code><span class="sig-paren">(</span><em class="sig-param">multiplier=1.0</em>, <em class="sig-param">classes=1000</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/mobilenet.html#MobileNet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.MobileNet" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.block.HybridBlock</span></code></p> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.MobileNet.hybrid_forward" title="mxnet.gluon.model_zoo.vision.MobileNet.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, x)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>MobileNet model from the |
| <a class="reference external" href="https://arxiv.org/abs/1704.04861">“MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>multiplier</strong> (<em>float</em><em>, </em><em>default 1.0</em>) – The width multiplier for controling the model size. Only multipliers that are no |
| less than 0.25 are supported. The actual number of channels is equal to the original |
| channel size multiplied by this multiplier.</p></li> |
| <li><p><strong>classes</strong> (<em>int</em><em>, </em><em>default 1000</em>) – Number of classes for the output layer.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.model_zoo.vision.MobileNet.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">x</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/mobilenet.html#MobileNet.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.MobileNet.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.model_zoo.vision.MobileNetV2"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">MobileNetV2</code><span class="sig-paren">(</span><em class="sig-param">multiplier=1.0</em>, <em class="sig-param">classes=1000</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/mobilenet.html#MobileNetV2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.MobileNetV2" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.block.HybridBlock</span></code></p> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.MobileNetV2.hybrid_forward" title="mxnet.gluon.model_zoo.vision.MobileNetV2.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, x)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>MobileNetV2 model from the |
| <a class="reference external" href="https://arxiv.org/abs/1801.04381">“Inverted Residuals and Linear Bottlenecks: |
| Mobile Networks for Classification, Detection and Segmentation”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>multiplier</strong> (<em>float</em><em>, </em><em>default 1.0</em>) – The width multiplier for controling the model size. The actual number of channels |
| is equal to the original channel size multiplied by this multiplier.</p></li> |
| <li><p><strong>classes</strong> (<em>int</em><em>, </em><em>default 1000</em>) – Number of classes for the output layer.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.model_zoo.vision.MobileNetV2.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">x</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/mobilenet.html#MobileNetV2.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.MobileNetV2.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.model_zoo.vision.ResNetV1"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">ResNetV1</code><span class="sig-paren">(</span><em class="sig-param">block</em>, <em class="sig-param">layers</em>, <em class="sig-param">channels</em>, <em class="sig-param">classes=1000</em>, <em class="sig-param">thumbnail=False</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/resnet.html#ResNetV1"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.ResNetV1" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.block.HybridBlock</span></code></p> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.ResNetV1.hybrid_forward" title="mxnet.gluon.model_zoo.vision.ResNetV1.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, x)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>ResNet V1 model from |
| <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>block</strong> (<a class="reference internal" href="../hybrid_block.html#mxnet.gluon.HybridBlock" title="mxnet.gluon.HybridBlock"><em>gluon.HybridBlock</em></a>) – Class for the residual block. Options are BasicBlockV1, BottleneckV1.</p></li> |
| <li><p><strong>layers</strong> (<em>list of int</em>) – Numbers of layers in each block</p></li> |
| <li><p><strong>channels</strong> (<em>list of int</em>) – Numbers of channels in each block. Length should be one larger than layers list.</p></li> |
| <li><p><strong>classes</strong> (<em>int</em><em>, </em><em>default 1000</em>) – Number of classification classes.</p></li> |
| <li><p><strong>thumbnail</strong> (<em>bool</em><em>, </em><em>default False</em>) – Enable thumbnail.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.model_zoo.vision.ResNetV1.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">x</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/resnet.html#ResNetV1.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.ResNetV1.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.model_zoo.vision.ResNetV2"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">ResNetV2</code><span class="sig-paren">(</span><em class="sig-param">block</em>, <em class="sig-param">layers</em>, <em class="sig-param">channels</em>, <em class="sig-param">classes=1000</em>, <em class="sig-param">thumbnail=False</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/resnet.html#ResNetV2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.ResNetV2" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.block.HybridBlock</span></code></p> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.ResNetV2.hybrid_forward" title="mxnet.gluon.model_zoo.vision.ResNetV2.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, x)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>ResNet V2 model from |
| <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>block</strong> (<a class="reference internal" href="../hybrid_block.html#mxnet.gluon.HybridBlock" title="mxnet.gluon.HybridBlock"><em>gluon.HybridBlock</em></a>) – Class for the residual block. Options are BasicBlockV1, BottleneckV1.</p></li> |
| <li><p><strong>layers</strong> (<em>list of int</em>) – Numbers of layers in each block</p></li> |
| <li><p><strong>channels</strong> (<em>list of int</em>) – Numbers of channels in each block. Length should be one larger than layers list.</p></li> |
| <li><p><strong>classes</strong> (<em>int</em><em>, </em><em>default 1000</em>) – Number of classification classes.</p></li> |
| <li><p><strong>thumbnail</strong> (<em>bool</em><em>, </em><em>default False</em>) – Enable thumbnail.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.model_zoo.vision.ResNetV2.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">x</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/resnet.html#ResNetV2.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.ResNetV2.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.model_zoo.vision.SqueezeNet"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">SqueezeNet</code><span class="sig-paren">(</span><em class="sig-param">version</em>, <em class="sig-param">classes=1000</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/squeezenet.html#SqueezeNet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.SqueezeNet" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.block.HybridBlock</span></code></p> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.SqueezeNet.hybrid_forward" title="mxnet.gluon.model_zoo.vision.SqueezeNet.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, x)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>SqueezeNet model from the <a class="reference external" href="https://arxiv.org/abs/1602.07360">“SqueezeNet: AlexNet-level accuracy with 50x fewer parameters |
| and <0.5MB model size”</a> paper. |
| SqueezeNet 1.1 model from the <a class="reference external" href="https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1">official SqueezeNet repo</a>. |
| SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters |
| than SqueezeNet 1.0, without sacrificing accuracy.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>version</strong> (<em>str</em>) – Version of squeezenet. Options are ‘1.0’, ‘1.1’.</p></li> |
| <li><p><strong>classes</strong> (<em>int</em><em>, </em><em>default 1000</em>) – Number of classification classes.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.model_zoo.vision.SqueezeNet.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">x</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/squeezenet.html#SqueezeNet.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.SqueezeNet.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.gluon.model_zoo.vision.VGG"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">VGG</code><span class="sig-paren">(</span><em class="sig-param">layers</em>, <em class="sig-param">filters</em>, <em class="sig-param">classes=1000</em>, <em class="sig-param">batch_norm=False</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/vgg.html#VGG"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.VGG" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.block.HybridBlock</span></code></p> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.model_zoo.vision.VGG.hybrid_forward" title="mxnet.gluon.model_zoo.vision.VGG.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, x)</p></td> |
| <td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p>VGG model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>layers</strong> (<em>list of int</em>) – Numbers of layers in each feature block.</p></li> |
| <li><p><strong>filters</strong> (<em>list of int</em>) – Numbers of filters in each feature block. List length should match the layers.</p></li> |
| <li><p><strong>classes</strong> (<em>int</em><em>, </em><em>default 1000</em>) – Number of classification classes.</p></li> |
| <li><p><strong>batch_norm</strong> (<em>bool</em><em>, </em><em>default False</em>) – Use batch normalization.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.gluon.model_zoo.vision.VGG.hybrid_forward"> |
| <code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">x</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/vgg.html#VGG.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.VGG.hybrid_forward" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>x</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li> |
| <li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.alexnet"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">alexnet</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">ctx=cpu(0)</em>, <em class="sig-param">root='/home/jenkins_slave/.mxnet/models'</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/alexnet.html#alexnet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.alexnet" title="Permalink to this definition">¶</a></dt> |
| <dd><p>AlexNet model from the <a class="reference external" href="https://arxiv.org/abs/1404.5997">“One weird trick…”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default $MXNET_HOME/models</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.densenet121"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">densenet121</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/densenet.html#densenet121"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.densenet121" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Densenet-BC 121-layer model from the |
| <a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.densenet161"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">densenet161</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/densenet.html#densenet161"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.densenet161" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Densenet-BC 161-layer model from the |
| <a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.densenet169"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">densenet169</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/densenet.html#densenet169"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.densenet169" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Densenet-BC 169-layer model from the |
| <a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.densenet201"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">densenet201</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/densenet.html#densenet201"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.densenet201" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Densenet-BC 201-layer model from the |
| <a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">“Densely Connected Convolutional Networks”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.get_mobilenet"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">get_mobilenet</code><span class="sig-paren">(</span><em class="sig-param">multiplier</em>, <em class="sig-param">pretrained=False</em>, <em class="sig-param">ctx=cpu(0)</em>, <em class="sig-param">root='/home/jenkins_slave/.mxnet/models'</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/mobilenet.html#get_mobilenet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.get_mobilenet" title="Permalink to this definition">¶</a></dt> |
| <dd><p>MobileNet model from the |
| <a class="reference external" href="https://arxiv.org/abs/1704.04861">“MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>multiplier</strong> (<em>float</em>) – The width multiplier for controling the model size. Only multipliers that are no |
| less than 0.25 are supported. The actual number of channels is equal to the original |
| channel size multiplied by this multiplier.</p></li> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default $MXNET_HOME/models</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.get_mobilenet_v2"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">get_mobilenet_v2</code><span class="sig-paren">(</span><em class="sig-param">multiplier</em>, <em class="sig-param">pretrained=False</em>, <em class="sig-param">ctx=cpu(0)</em>, <em class="sig-param">root='/home/jenkins_slave/.mxnet/models'</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/mobilenet.html#get_mobilenet_v2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.get_mobilenet_v2" title="Permalink to this definition">¶</a></dt> |
| <dd><p>MobileNetV2 model from the |
| <a class="reference external" href="https://arxiv.org/abs/1801.04381">“Inverted Residuals and Linear Bottlenecks: |
| Mobile Networks for Classification, Detection and Segmentation”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>multiplier</strong> (<em>float</em>) – The width multiplier for controling the model size. Only multipliers that are no |
| less than 0.25 are supported. The actual number of channels is equal to the original |
| channel size multiplied by this multiplier.</p></li> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default $MXNET_HOME/models</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.get_model"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">get_model</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision.html#get_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.get_model" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns a pre-defined model by name</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Name of the model.</p></li> |
| <li><p><strong>pretrained</strong> (<em>bool</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>classes</strong> (<em>int</em>) – Number of classes for the output layer.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The model.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../hybrid_block.html#mxnet.gluon.HybridBlock" title="mxnet.gluon.HybridBlock">gluon.HybridBlock</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.get_resnet"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">get_resnet</code><span class="sig-paren">(</span><em class="sig-param">version</em>, <em class="sig-param">num_layers</em>, <em class="sig-param">pretrained=False</em>, <em class="sig-param">ctx=cpu(0)</em>, <em class="sig-param">root='/home/jenkins_slave/.mxnet/models'</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/resnet.html#get_resnet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.get_resnet" title="Permalink to this definition">¶</a></dt> |
| <dd><p>ResNet V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper. |
| ResNet V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>version</strong> (<em>int</em>) – Version of ResNet. Options are 1, 2.</p></li> |
| <li><p><strong>num_layers</strong> (<em>int</em>) – Numbers of layers. Options are 18, 34, 50, 101, 152.</p></li> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default $MXNET_HOME/models</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.get_vgg"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">get_vgg</code><span class="sig-paren">(</span><em class="sig-param">num_layers</em>, <em class="sig-param">pretrained=False</em>, <em class="sig-param">ctx=cpu(0)</em>, <em class="sig-param">root='/home/jenkins_slave/.mxnet/models'</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/vgg.html#get_vgg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.get_vgg" title="Permalink to this definition">¶</a></dt> |
| <dd><p>VGG model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>num_layers</strong> (<em>int</em>) – Number of layers for the variant of densenet. Options are 11, 13, 16, 19.</p></li> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default $MXNET_HOME/models</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.inception_v3"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">inception_v3</code><span class="sig-paren">(</span><em class="sig-param">pretrained=False</em>, <em class="sig-param">ctx=cpu(0)</em>, <em class="sig-param">root='/home/jenkins_slave/.mxnet/models'</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/inception.html#inception_v3"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.inception_v3" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Inception v3 model from |
| <a class="reference external" href="http://arxiv.org/abs/1512.00567">“Rethinking the Inception Architecture for Computer Vision”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default $MXNET_HOME/models</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.mobilenet0_25"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">mobilenet0_25</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/mobilenet.html#mobilenet0_25"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.mobilenet0_25" title="Permalink to this definition">¶</a></dt> |
| <dd><p>MobileNet model from the |
| <a class="reference external" href="https://arxiv.org/abs/1704.04861">“MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”</a> paper, with width multiplier 0.25.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.mobilenet0_5"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">mobilenet0_5</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/mobilenet.html#mobilenet0_5"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.mobilenet0_5" title="Permalink to this definition">¶</a></dt> |
| <dd><p>MobileNet model from the |
| <a class="reference external" href="https://arxiv.org/abs/1704.04861">“MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”</a> paper, with width multiplier 0.5.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.mobilenet0_75"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">mobilenet0_75</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/mobilenet.html#mobilenet0_75"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.mobilenet0_75" title="Permalink to this definition">¶</a></dt> |
| <dd><p>MobileNet model from the |
| <a class="reference external" href="https://arxiv.org/abs/1704.04861">“MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”</a> paper, with width multiplier 0.75.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.mobilenet1_0"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">mobilenet1_0</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/mobilenet.html#mobilenet1_0"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.mobilenet1_0" title="Permalink to this definition">¶</a></dt> |
| <dd><p>MobileNet model from the |
| <a class="reference external" href="https://arxiv.org/abs/1704.04861">“MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”</a> paper, with width multiplier 1.0.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.mobilenet_v2_0_25"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">mobilenet_v2_0_25</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/mobilenet.html#mobilenet_v2_0_25"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.mobilenet_v2_0_25" title="Permalink to this definition">¶</a></dt> |
| <dd><p>MobileNetV2 model from the |
| <a class="reference external" href="https://arxiv.org/abs/1801.04381">“Inverted Residuals and Linear Bottlenecks: |
| Mobile Networks for Classification, Detection and Segmentation”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.mobilenet_v2_0_5"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">mobilenet_v2_0_5</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/mobilenet.html#mobilenet_v2_0_5"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.mobilenet_v2_0_5" title="Permalink to this definition">¶</a></dt> |
| <dd><p>MobileNetV2 model from the |
| <a class="reference external" href="https://arxiv.org/abs/1801.04381">“Inverted Residuals and Linear Bottlenecks: |
| Mobile Networks for Classification, Detection and Segmentation”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.mobilenet_v2_0_75"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">mobilenet_v2_0_75</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/mobilenet.html#mobilenet_v2_0_75"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.mobilenet_v2_0_75" title="Permalink to this definition">¶</a></dt> |
| <dd><p>MobileNetV2 model from the |
| <a class="reference external" href="https://arxiv.org/abs/1801.04381">“Inverted Residuals and Linear Bottlenecks: |
| Mobile Networks for Classification, Detection and Segmentation”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.mobilenet_v2_1_0"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">mobilenet_v2_1_0</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/mobilenet.html#mobilenet_v2_1_0"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.mobilenet_v2_1_0" title="Permalink to this definition">¶</a></dt> |
| <dd><p>MobileNetV2 model from the |
| <a class="reference external" href="https://arxiv.org/abs/1801.04381">“Inverted Residuals and Linear Bottlenecks: |
| Mobile Networks for Classification, Detection and Segmentation”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.resnet101_v1"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">resnet101_v1</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/resnet.html#resnet101_v1"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.resnet101_v1" title="Permalink to this definition">¶</a></dt> |
| <dd><p>ResNet-101 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.resnet101_v2"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">resnet101_v2</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/resnet.html#resnet101_v2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.resnet101_v2" title="Permalink to this definition">¶</a></dt> |
| <dd><p>ResNet-101 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.resnet152_v1"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">resnet152_v1</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/resnet.html#resnet152_v1"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.resnet152_v1" title="Permalink to this definition">¶</a></dt> |
| <dd><p>ResNet-152 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.resnet152_v2"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">resnet152_v2</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/resnet.html#resnet152_v2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.resnet152_v2" title="Permalink to this definition">¶</a></dt> |
| <dd><p>ResNet-152 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.resnet18_v1"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">resnet18_v1</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/resnet.html#resnet18_v1"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.resnet18_v1" title="Permalink to this definition">¶</a></dt> |
| <dd><p>ResNet-18 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.resnet18_v2"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">resnet18_v2</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/resnet.html#resnet18_v2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.resnet18_v2" title="Permalink to this definition">¶</a></dt> |
| <dd><p>ResNet-18 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.resnet34_v1"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">resnet34_v1</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/resnet.html#resnet34_v1"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.resnet34_v1" title="Permalink to this definition">¶</a></dt> |
| <dd><p>ResNet-34 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.resnet34_v2"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">resnet34_v2</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/resnet.html#resnet34_v2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.resnet34_v2" title="Permalink to this definition">¶</a></dt> |
| <dd><p>ResNet-34 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.resnet50_v1"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">resnet50_v1</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/resnet.html#resnet50_v1"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.resnet50_v1" title="Permalink to this definition">¶</a></dt> |
| <dd><p>ResNet-50 V1 model from <a class="reference external" href="http://arxiv.org/abs/1512.03385">“Deep Residual Learning for Image Recognition”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.resnet50_v2"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">resnet50_v2</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/resnet.html#resnet50_v2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.resnet50_v2" title="Permalink to this definition">¶</a></dt> |
| <dd><p>ResNet-50 V2 model from <a class="reference external" href="https://arxiv.org/abs/1603.05027">“Identity Mappings in Deep Residual Networks”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.squeezenet1_0"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">squeezenet1_0</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/squeezenet.html#squeezenet1_0"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.squeezenet1_0" title="Permalink to this definition">¶</a></dt> |
| <dd><p>SqueezeNet 1.0 model from the <a class="reference external" href="https://arxiv.org/abs/1602.07360">“SqueezeNet: AlexNet-level accuracy with 50x fewer parameters |
| and <0.5MB model size”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.squeezenet1_1"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">squeezenet1_1</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/squeezenet.html#squeezenet1_1"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.squeezenet1_1" title="Permalink to this definition">¶</a></dt> |
| <dd><p>SqueezeNet 1.1 model from the <a class="reference external" href="https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1">official SqueezeNet repo</a>. |
| SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters |
| than SqueezeNet 1.0, without sacrificing accuracy.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.vgg11"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">vgg11</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/vgg.html#vgg11"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.vgg11" title="Permalink to this definition">¶</a></dt> |
| <dd><p>VGG-11 model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.vgg11_bn"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">vgg11_bn</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/vgg.html#vgg11_bn"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.vgg11_bn" title="Permalink to this definition">¶</a></dt> |
| <dd><p>VGG-11 model with batch normalization from the |
| <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.vgg13"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">vgg13</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/vgg.html#vgg13"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.vgg13" title="Permalink to this definition">¶</a></dt> |
| <dd><p>VGG-13 model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.vgg13_bn"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">vgg13_bn</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/vgg.html#vgg13_bn"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.vgg13_bn" title="Permalink to this definition">¶</a></dt> |
| <dd><p>VGG-13 model with batch normalization from the |
| <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.vgg16"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">vgg16</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/vgg.html#vgg16"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.vgg16" title="Permalink to this definition">¶</a></dt> |
| <dd><p>VGG-16 model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.vgg16_bn"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">vgg16_bn</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/vgg.html#vgg16_bn"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.vgg16_bn" title="Permalink to this definition">¶</a></dt> |
| <dd><p>VGG-16 model with batch normalization from the |
| <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.vgg19"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">vgg19</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/vgg.html#vgg19"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.vgg19" title="Permalink to this definition">¶</a></dt> |
| <dd><p>VGG-19 model from the <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.gluon.model_zoo.vision.vgg19_bn"> |
| <code class="sig-prename descclassname">mxnet.gluon.model_zoo.vision.</code><code class="sig-name descname">vgg19_bn</code><span class="sig-paren">(</span><em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/model_zoo/vision/vgg.html#vgg19_bn"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.model_zoo.vision.vgg19_bn" title="Permalink to this definition">¶</a></dt> |
| <dd><p>VGG-19 model with batch normalization from the |
| <a class="reference external" href="https://arxiv.org/abs/1409.1556">“Very Deep Convolutional Networks for Large-Scale Image Recognition”</a> paper.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to load the pretrained weights for model.</p></li> |
| <li><p><strong>ctx</strong> (<a class="reference internal" href="../../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a><em>, </em><em>default CPU</em>) – The context in which to load the pretrained weights.</p></li> |
| <li><p><strong>root</strong> (<em>str</em><em>, </em><em>default '$MXNET_HOME/models'</em>) – Location for keeping the model parameters.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
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| <li><a class="reference internal" href="#">gluon.model_zoo.vision</a><ul> |
| <li><a class="reference internal" href="#resnet">ResNet</a></li> |
| <li><a class="reference internal" href="#id17">VGG</a></li> |
| <li><a class="reference internal" href="#id27">Alexnet</a></li> |
| <li><a class="reference internal" href="#id29">DenseNet</a></li> |
| <li><a class="reference internal" href="#id34">SqueezeNet</a></li> |
| <li><a class="reference internal" href="#inception">Inception</a></li> |
| <li><a class="reference internal" href="#id38">MobileNet</a></li> |
| <li><a class="reference internal" href="#module-mxnet.gluon.model_zoo.vision">API Reference</a></li> |
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