py-faster-rcnn-resnet-imagenet
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Faster RCNN with ResNet for ImageNet Detection
Faster RCNN with ResNet for ImageNet Detection
Based on Faster RCNN, the repository aims to reproduce the ImageNet Detection results in ResNet paper (Deep Residual Learning for Image Recognition).
So far, it achieves mAP 52.2% (vs. 60.5% in the paper) on val2 of ImageNet 2015 Detection dataset without the use of Box refinement, Global context and Multi-scale testing.
If you have any questions about this code, please create an issue.
What has been done
- Add ImageNet Detection imdb and roidb
- Use ResNet-101
- Multi-gpus training and testing
- Fix some problems when training Faster RCNN over ImageNet
What has NOT been done
- Box refinement
- Global context
- Multi-scale testing
If you are interested in these, please feel free to submit PRs.
How to use this code
If you find any directory in the following steps does not exist, please create it by yourself.
Test with trained models
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Clone this repository and set up the environment as described in the original README.md of Faster RCNN
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Download the models I trained. Populate them into
output/faster_rcnn_alt_opt/imagenet_2015_trainval1_woextra
. -
Generate RPN proposals
python tools/rpn_generate.py --def models/imagenet/ResNet-101/faster_rcnn_alt_opt/rpn_test.pt --net output/faster_rcnn_alt_opt/imagenet_2015_trainval1_woextra/resnet-101_rpn_stage1_iter_320000.caffemodel --imdb imagenet_2015_val2 --gpu 0 1 2 3
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Use Fast RCNN to detect objects with these proposals generated by RPN
./tools/test_net.py --gpu 0 1 2 3 --def models/imagenet/ResNet-101/fast_rcnn/test.prototxt --net output/faster_rcnn_alt_opt/imagenet_2015_trainval1_woextra/resnet-101_fast_rcnn_stage1_iter_320000.caffemodel --imdb imagenet_2015_val2 --cfg experiments/cfgs/fast_rcnn.yml --rpn_file output/default/imagenet_2015_val2/resnet-101_rpn_stage1_iter_320000/proposals_test
Once it finishes, you could see the final mAP over val2.
Train it by yourself
Hardware required: 4 or 8 GPU cards are required to train the model because the batch sizes used in the original paper are 8 for RPN and 16 for Fast RCNN.
Please note: because the improvement of stage 3 and 4 is minior, only the first two stages are trained. With 8 Titanx GPU cards, it takes about four days to train.
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Download ImageNet Detection 2015 datasets, and unzip it into directory
data
. It should have this basic structureImageNet2015/Annotations ImageNet2015/Data ImageNet2015/ImageSets # others...
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Run
cp trainval1_woextra.txt val2.txt data/ImageNet2015/ImageSets/DET
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Download the pretranied model of ResNet-101 here, and populate it into
data/imagenet_models
with nameResNet-101.caffemodel
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Run
experiments/scripts/faster_rcnn_alt_opt.sh 0,1,2,3,4,5,6,7 ResNet-101 imagenet
to train.0,1,2,3,4,5,6,7
is GPU ids. Please replace it if you use other GPUs.