wsddn.pytorch
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adursun
Implementation of Weakly Supervised Deep Detection Networks using the latest version of PyTorch
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/bash
Training Steps
# for VGG based model
python src/train.py --base_net vgg
Evaluation 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
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