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# COCO Object detection with RevCol
## Getting started
We build RevCol object detection model based on [mmdetection](https://github.com/open-mmlab/mmdetection/tree/3e2693151add9b5d6db99b944da020cba837266b) commit `3e26931`. We add RevCol model and config files to [the original repo](https://github.com/open-mmlab/mmdetection/tree/3e2693151add9b5d6db99b944da020cba837266b). Please refer to [get_started.md](https://github.com/open-mmlab/mmdetection/blob/3e2693151add9b5d6db99b944da020cba837266b/docs/en/get_started.md) for installation and dataset preparation instructions.
## Results and Fine-tuned Models
| name | Pretrained Model | Method | Lr Schd | box mAP | mask mAP | #params | FLOPs | Fine-tuned Model |
|:---:|:---:|:---:|:---:| :---:|:---:|:---:|:---:| :---:|
| RevCol-T | [ImageNet-1K]() | Cascade Mask R-CNN | 3x | 50.6 | 43.8 | 88M | 741G | [model]() |
| RevCol-S | [ImageNet-1K]() | Cascade Mask R-CNN | 3x | 52.6 | 45.5 | 118M | 833G | [model]() |
| RevCol-B | [ImageNet-1K]() | Cascade Mask R-CNN | 3x | 53.0 | 45.9 | 196M | 988G | [model]() |
| RevCol-B | [ImageNet-22K]() | Cascade Mask R-CNN | 3x | 55.0 | 47.5 | 196M | 988G | [model]() |
| RevCol-L | [ImageNet-22K]() | Cascade Mask R-CNN | 3x | 55.9 | 48.4 | 330M | 1453G | [model]() |
## Training
To train a detector with pre-trained models, run:
```
# single-gpu training
python tools/train.py <CONFIG_FILE> --cfg-options model.pretrained=<PRETRAIN_MODEL> [other optional arguments]
# multi-gpu training
tools/dist_train.sh <CONFIG_FILE> <GPU_NUM> --cfg-options model.pretrained=<PRETRAIN_MODEL> [other optional arguments]
```
For example, to train a Cascade Mask R-CNN model with a `RevCol-T` backbone and 8 gpus, run:
```
tools/dist_train.sh configs/revcol/cascade_mask_rcnn_revcol_tiny_3x_in1k.py 8 --cfg-options pretrained=<PRETRAIN_MODEL>
```
More config files can be found at [`configs/revcol`](configs/revcol).
## Inference
```
# single-gpu testing
python tools/test.py <CONFIG_FILE> <DET_CHECKPOINT_FILE> --eval bbox segm
# multi-gpu testing
tools/dist_test.sh <CONFIG_FILE> <DET_CHECKPOINT_FILE> <GPU_NUM> --eval bbox segm
```
## Acknowledgment
This code is built using [mmdetection](https://github.com/open-mmlab/mmdetection), [timm](https://github.com/rwightman/pytorch-image-models) libraries, and [BeiT](https://github.com/microsoft/unilm/tree/f8f3df80c65eb5e5fc6d6d3c9bd3137621795d1e/beit), [Swin Transformer](https://github.com/microsoft/Swin-Transformer) repositories. |