| # Use parameters pre-trained from Caffe in SINGA |
| |
| In this example, we use SINGA to load the VGG parameters trained by Caffe to do image classification. |
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| ## Run this example |
| You can run this example by simply executing `run.sh vgg16` or `run.sh vgg19` |
| The script does the following work. |
| |
| ### Obtain the Caffe model |
| * Download caffe model prototxt and parameter binary file. |
| * Currently we only support the latest caffe format, if your model is in |
| previous version of caffe, please update it to current format.(This is |
| supported by caffe) |
| * After updating, we can obtain two files, i.e., the prototxt and parameter |
| binary file. |
| |
| ### Prepare test images |
| A few sample images are downloaded into the `test` folder. |
| |
| ### Predict |
| The `predict.py` script creates the VGG model and read the parameters, |
| |
| usage: predict.py [-h] model_txt model_bin imgclass |
| |
| where `imgclass` refers to the synsets of imagenet dataset for vgg models. |
| You can start the prediction program by executing the following command: |
| |
| python predict.py vgg16.prototxt vgg16.caffemodel synset_words.txt |
| |
| Then you type in the image path, and the program would output the top-5 labels. |
| |
| More Caffe models would be tested soon. |
