WSDDN PyTorch
Implementation of Weakly Supervised Deep Detection Networks using the latest version of PyTorch.
Bilen, H., & Vedaldi, A. (2016). Weakly supervised deep detection networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2846-2854).
Implementation Differences
- Adam optimizer (instead of SGD)
- Spatial regulariser isn't added
Experiments
VGG16based model is closest toEB + Box Sc.case with model L, which reported 30.4 mAP in the paperAlexNetbased model is closest toEB + Box Sc.case with model S, which reported 33.4 mAP in the paper- Results when
VGG16is used as base model
| aero | bike | bird | boat | bottle | bus | car | cat | chair | cow | table | dog | horse | mbike | person | plant | sheep | sofa | train | tv | mean |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 41.4 | 46.3 | 22.7 | 24.5 | 13.6 | 57.7 | 49.9 | 31.1 | 7.5 | 31.1 | 24.3 | 25.9 | 38.7 | 53.5 | 7.2 | 13.9 | 31.1 | 38.6 | 48.3 | 39.0 | 32.3 |
- Results when
AlexNetis used as base model
| aero | bike | bird | boat | bottle | bus | car | cat | chair | cow | table | dog | horse | mbike | person | plant | sheep | sofa | train | tv | mean |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38.1 | 41.5 | 27.1 | 18.6 | 10.3 | 48.8 | 47.6 | 36.8 | 1.6 | 25.9 | 28.5 | 30.4 | 39.7 | 46.8 | 15.1 | 12.4 | 28.3 | 32.4 | 44.2 | 44.8 | 30.9 |
Requirements
- Docker (19.03.2)
- nvidia-container-toolkit (https://github.com/NVIDIA/nvidia-docker)
Build Steps
git clone [email protected]:adursun/wsddn.pytorch.git
cd wsddn.pytorch
./prepare.sh
docker run --rm --gpus all --ipc=host -v `pwd`:/ws -it wsddn.pytorch /bin/bashTraining Steps
# for VGG based model
python src/train.py --base_net vggEvaluation Steps
# for VGG based model
# run `wget "https://www.dropbox.com/s/xyi4hgms6y3ldmj/vgg_epoch_20.pt?dl=1" -P states/` to use pretrained weights
python src/evaluate.py --base_net vgg --state_path states/vgg_epoch_20.pt