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RetinaMask

The code is based on the maskrcnn-benchmark.

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Citing RetinaMask

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}
}

Installation
Models

Installation

Follow the maskrcnn-benchmark to install code and set up the dataset. Use config files in ./configs/retina/ for Training and Testing.

Models

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.

Run Inference

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