We use Mask-RCNN to evaluate the down-stream performance. Please refer to the paper for detailed settings.
First follow the installation guidance in the main readme file to prepare the environment. For object detection, additional packages shall be installed:
- mmcv (1.x.0) installation guidance
- mmdet (1.x.0) installation guidance Other version may also work.
| Scale | Model | Box AP | Mask AP | checkpoint |
|---|---|---|---|---|
| Micro | U-HaloNet | 40.3 | 37.3 | Download |
| U-PVT | 35.9 | 34.2 | Download | |
| U-Swin Transformer | 36.6 | 34.6 | Download | |
| U-ConvNeXt | 39.2 | 36.4 | Download | |
| U-InternImage | 39.5 | 36.6 | Download | |
| Tiny | U-HaloNet | 46.9 | 42.4 | Download |
| U-PVT | 44.2 | 40.6 | Download | |
| U-Swin Transformer | 44.3 | 40.5 | Download | |
| U-ConvNeXt | 44.3 | 40.5 | Download | |
| U-InternImage | 47.2 | 42.5 | Download | |
| Small | U-HaloNet | 48.2 | 43.3 | Download |
| U-PVT | 46.1 | 41.9 | Download | |
| U-Swin Transformer | 46.4 | 42.1 | Download | |
| U-ConvNeXt | 45.6 | 41.2 | Download | |
| U-InternImage | 47.8 | 43.0 | Download | |
| Base | U-HaloNet | 49.0 | 43.8 | Download |
| U-PVT | 46.4 | 42.3 | Download | |
| U-Swin Transformer | 47.0 | 42.2 | Download | |
| U-ConvNeXt | 46.7 | 42.2 | Download | |
| U-InternImage | 48.7 | 43.8 | Download |
- To train a mask rcnn based on unified-swin with slurm
- remember to modify the path to the pertained ckpt in
./configs/unified_models/
- remember to modify the path to the pertained ckpt in
# MODEL_TYPE: halonet, pvt, swin, convnext
# SCALE: micro, tiny, small, base
bash shells/train.py [MODEL_TYPE] [SCALE]- For training on a single machine, run the following command:
bash shells/dist_train.py [CONFIG] [NUM_GPUS] --auto-scale-lr- To test the trained model with slurm
bash shells/test.py [MODEL_TYPE] [SCALE]- For single machine evaluation, run the following command:
bash shells/dist_test.py [CONFIG] [CKPT_PATH] [NUM_GPUS]