
# RepPoints V2


## Installation

Please refer to [INSTALL.md](INSTALL.md) for installation and dataset preparation.

## Evaluation

We provide 3 pretrained model for evaluation. The result is 0.1 lower than then number we reported in the paper for we retrained the models.

| Backbone | Schedule. | Config File | Expected mAP | Model
| :-----: | :---: | :----------: | :----: | :----: |
| ResNet-50-FPN    |   1x  | configs/reppoints_v2_r50_1x.py     | 40.9 | [Google](https://drive.google.com/file/d/1goQIA-j400t_Ii-yV52rI_2Ip47vbIcL/view?usp=sharing)
| ResNet-50-FPN    |   2x  | configs/reppoints_v2_r50_2x_mt.py  | 43.8 | [Google](https://drive.google.com/file/d/1GnV679VlLz7tYVi3SoYseMTp0tBq5GDc/view?usp=sharing)
| ResNet-101-FPN   |   2x  | configs/reppoints_v2_r101_2x_mt.py | 45.4 | [Google](https://drive.google.com/file/d/1lLfGS1U_bQ2iIvnAxq_QhFWDWBH1jIcn/view?usp=sharing)

Run the following command to test on COCO val2017 split.

```bash
cd path_to_reppoints_v2
export NGPUS=4  # (or 8)
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/test.py "path/to/config/file.py" "path/to/.pth file" --out bbox.pkl --eval bbox --launcher pytorch
```