Our detection implementation is based on MMDetection and PVT detection. Thank the authors for their wonderful works.
Please note that we just simply follow the hyper-parameters of PVT which may not be the optimal ones for Context Cluster. Feel free to tune the hyper-parameters to get better performance.
Install MMDetection from souce cocde,
or
pip install mmdet --user
Prepare COCO according to the guidelines in MMDetection.
| Backbone | Parmas | AP-box | AP-box@50 | AP-box@75 | AP-mask | AP-mask@50 | AP-mask@75 | Download |
|---|---|---|---|---|---|---|---|---|
| ResNet18 | 31.2M | 34.0 | 54.0 | 36.7 | 31.2 | 51.0 | 32.7 | |
| PVT-Tiny | 32.9M | 36.7 | 59.2 | 39.3 | 35.1 | 56.7 | 37.3 | |
| CoC-small-4 | 33.6M | 35.9 | 58.3 | 38.3 | 33.8 | 55.3 | 35.8 | [model] |
| CoC-small-25 | 33.6M | 37.5 | 60.1 | 40.0 | 35.4 | 57.1 | 37.9 | [model] |
| CoC-small-49 | 33.6M | 37.2 | 59.8 | 39.7 | 34.9 | 56.7 | 37.0 | [model] |
| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |
| ResNet50 | 44.2M | 38.0 | 58.6 | 41.4 | 34.4 | 55.1 | 36.7 | |
| PVT-Small | 44.1M | 40.4 | 62.9 | 43.8 | 37.8 | 60.1 | 40.3 | |
| CoC-medium-4 | 42.1M | 38.6 | 61.1 | 41.5 | 36.1 | 58.2 | 38.0 | [model] |
| CoC-medium-25 | 42.1M | 40.1 | 62.8 | 43.6 | 37.4 | 59.9 | 40.0 | [model] |
| CoC-medium-49 | 42.1M | 40.6 | 63.3 | 43.9 | 37.6 | 60.1 | 39.9 | [model] |
To evaluate Context Cluster + Mask R-CNN on COCO val2017, run:
dist_test.sh configs/{configure-file} /path/to/checkpoint_file 8 --out results.pkl --eval bbox segm
To train Context Cluster + Mask R-CNN on COCO train2017:
dist_train.sh configs/{configure-file} 8