Model | Training data | Test data | mAP | Note |
---|---|---|---|---|
VGG16_reduced 300x300 | VOC07+12 trainval | VOC07 test | 77.8 | fast |
VGG16_reduced 512x512 | VOC07+12 trainval | VOC07 test | 79.9 | slow |
Inception-v3 512x512 | VOC07+12 trainval | VOC07 test | 78.9 | fastest |
Resnet-50 512x512 | VOC07+12 trainval | VOC07 test | 78.9 | fast |
Model | GPU | CUDNN | Batch-size | FPS* |
---|---|---|---|---|
VGG16_reduced 300x300 | TITAN X(Maxwell) | v5.1 | 16 | 95 |
VGG16_reduced 300x300 | TITAN X(Maxwell) | v5.1 | 8 | 95 |
VGG16_reduced 300x300 | TITAN X(Maxwell) | v5.1 | 1 | 64 |
VGG16_reduced 300x300 | TITAN X(Maxwell) | N/A | 8 | 36 |
VGG16_reduced 300x300 | TITAN X(Maxwell) | N/A | 1 | 28 |
Forward time only, data loading and drawing excluded. |
cv2
, matplotlib
and numpy
. If you use mxnet-python api, you probably have already got them. You can install them via pip or package managers, such as apt-get
:sudo apt-get install python-opencv python-matplotlib python-numpy
# for Ubuntu/Debian cp make/config.mk ./config.mk # enable cuda, cudnn if applicable
Remember to enable CUDA if you want to be able to train, since CPU training is insanely slow. Using CUDNN is optional, but highly recommended.
ssd_resnet50_0712.zip
, and extract to model/
directory.# cd /path/to/mxnet-ssd python demo.py --gpu 0 # play with examples: python demo.py --epoch 0 --images ./data/demo/dog.jpg --thresh 0.5 python demo.py --cpu --network resnet50 --data-shape 512 # wait for library to load for the first time
python demo.py --help
for more options.This example only covers training on Pascal VOC dataset. Other datasets should be easily supported by adding subclass derived from class Imdb
in dataset/imdb.py
. See example of dataset/pascal_voc.py
for details.
vgg16_reduced
model here, unzip .param
and .json
files into model/
directory by default.cd /path/to/where_you_store_datasets/ wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar # Extract the data. tar -xvf VOCtrainval_11-May-2012.tar tar -xvf VOCtrainval_06-Nov-2007.tar tar -xvf VOCtest_06-Nov-2007.tar
trainval
set in VOC2007/2012 as a common strategy. The suggested directory structure is to store VOC2007
and VOC2012
directories in the same VOCdevkit
folder.VOCdevkit
folder to data/VOCdevkit
by default:ln -s /path/to/VOCdevkit /path/to/mxnet/example/ssd/data/VOCdevkit
Use hard link instead of copy could save us a bit disk space.
# cd /path/to/mxnet/example/ssd bash tools/prepare_pascal.sh # or if you are using windows python tools/prepare_dataset.py --dataset pascal --year 2007,2012 --set trainval --target ./data/train.lst python tools/prepare_dataset.py --dataset pascal --year 2007 --set test --target ./data/val.lst --shuffle False
# cd /path/to/mxnet/example/ssd python train.py
batch-size=32
and learning_rate=0.002
. You might need to change the parameters a bit if you have different configurations. Check python train.py --help
for more training options. For example, if you have 4 GPUs, use:# note that a perfect training parameter set is yet to be discovered for multi-GPUs python train.py --gpus 0,1,2,3 --batch-size 32
Make sure you have val.rec as validation dataset. It's the same one as used in training. Use:
# cd /path/to/mxnet/example/ssd python evaluate.py --gpus 0,1 --batch-size 128 --epoch 0
This simply removes all loss layers, and attach a layer for merging results and non-maximum suppression. Useful when loading python symbol is not available.
# cd /path/to/mxnet/example/ssd python deploy.py --num-class 20
Converter from caffe is available at /path/to/mxnet/example/ssd/tools/caffe_converter
This is specifically modified to handle custom layer in caffe-ssd. Usage:
cd /path/to/mxnet/example/ssd/tools/caffe_converter make python convert_model.py deploy.prototxt name_of_pretrained_caffe_model.caffemodel ssd_converted # you will use this model in deploy mode without loading from python symbol(layer names inconsistent) python demo.py --prefix ssd_converted --epoch 1 --deploy
There is no guarantee that conversion will always work, but at least it's good for now.
Since the new interface for composing network is introduced, the old models have inconsistent names for weights. You can still load the previous model by rename the symbol to legacy_xxx.py
and call with python train/demo.py --network legacy_xxx
For example:
python demo.py --network 'legacy_vgg16_ssd_300.py' --prefix model/ssd_300 --epoch 0