Awesome
Feature Selective Anchor-Free Module for Single-Shot Object Detection. CVPR, 2019. (in PyTorch)
Description
This repository reproduces "Zhu et al. Feature Selective Anchor-Free Module for Single-Shot Object Detection. CVPR, 2019." (FSAF) PDF in PyTorch. The implementation is based on MMDetection framework. All the codes for the FSAF model follow the original paper.
Get Started
To use this repo, please follow README.md of MMDetection.
Train/Eval
Train
- To train baseline (i.e., RetinaNet)
./tools/dist_train_retinanet_r50_400_050x.sh
- To train FSAF (w/o anchor-based (AB))
./tools/dist_train_fsaf_r50_400_050x.sh
Eval
For evaluation, pretrained model-weights should be located at "./models/here".
- To evaluate baseline (i.e., RetinaNet)
./tools/eval_retinanet_r50_400_050x.sh
- To evaluate FSAF (w/o anchor-based (AB))
./tools/eval_fsaf_r50_400_050x.sh
Benchmark
Below is benchmark results. All models are trained with an image-size of 400 and reduced LR-schedule for efficient experiments. Reproduced results show a similar aspect to the original paper (Table 1,2), demonstrating sanity of the implementation.
| model | backbone | img-size | LR-schd | box AP | box AP_50 | box AP_75 | download |
|---|---|---|---|---|---|---|---|
| RetinaNet | R-50-FPN | 400 | 0.50x | 26.0 | 43.4 | 27.1 | model |
| FSAF (w/o AB) | R-50-FPN | 400 | 0.50x | 26.2 | 44.7 | 26.5 | model |
TODO
- Code reorganization is needed to be consistent with the style of MMDetection framework (current code is only written for fast prototyping).
Contact
- Ho-Deok Jang
- Email: hdjang@kaist.ac.kr
- Homepage: https://sites.google.com/view/hdjangcv