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
- This implementation is closest to
EB + Box Sc.case with Large base model, which reported 30.4 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 |
Requirements
- Docker (19.03.2)
- nvidia-container-toolkit (https://github.com/NVIDIA/nvidia-docker)
Build Steps
./prepare.sh
docker run --rm --gpus all --ipc=host -v `pwd`:/ws -it wsddn.pytorch /bin/bash
Training Steps
python src/train.py
Evaluation Steps
python src/evaluate.py --state_path=<state_dict_path>