The code is based on the maskrcnn-benchmark.
Please cite RetinaMask in your publications if it helps your research:
@inproceedings{fu2019retinamask,
title = {{RetinaMask}: Learning to predict masks improves state-of-the-art single-shot detection for free},
author = {Fu, Cheng-Yang and Shvets, Mykhailo and Berg, Alexander C.},
booktitle = {arXiv preprint arXiv:1901.03353},
year = {2019}
}
Follow the maskrcnn-benchmark to install code and set up the dataset. Use config files in ./configs/retina/ for Training and Testing.
| Models | BBox | B(time) | Mask | M(time) | Link |
|---|---|---|---|---|---|
| ResNet-50-FPN | 39.4/58.6/42.3/21.9/42.0/51.0 | 0.124 | 34.9/55.7/37.1/15.1/36.7/50.4 | 0.139 | link |
| ResNet-101-FPN | 41.4/ 60.8/44.6/23.0/44.5/53.5 | 0.145 | 36.6/58.0/39.1/16.2/38.8/52.7 | 0.160 | link |
| ResNet-101-FPN-GN | 41.7/61.7/45.0/23.5/44.7/52.8 | 0.153 | 36.7/58.8/39.3/16.4/39.4/52.6 | 0.164 | link |
| ResNeXt32x8d-101-FPN-GN | 42.6/62.5/46.0/24.8/45.6/53.8 | 0.231 | 37.4/59.8/40.0/17.6/39.9/53.4 | 0.270 | link |
P.S. evaluation metric: AP, AP50, AP75, AP(small), AP(medium), AP(large), please refer to COCO for detailed explanation. The inference time is measured on Nvidia 1080Ti.
Use the following scripts. (Assume models are download to the ./models directory) Run Mask and BBox
python tools/test_net.py --config-file ./configs/retina/retinanet_mask_R-50-FPN_2x_adjust_std011_ms.yaml MODEL.WEIGHT ./models/retinanet_mask_R-50-FPN_2x_adjust_std011_ms_model.pth
Run BBox only
python tools/test_net.py --config-file ./configs/retina/retinanet_mask_R-50-FPN_2x_adjust_std011_ms.yaml MODEL.WEIGHT ./models/retinanet_mask_R-50-FPN_2x_adjust_std011_ms_model.pth MODEL.MASK_ON False