FocalNet/FocalNet-DINO

This repo contains the code and configuration files for reproducing object detection results of FocalNets with DINO

FocalNet for Object Detection with DINO

This repo contains the code for reproducing object detection results of our FocalNets. It is based on DINO.

Installation

Please follow DINO's instruction for installation.

Training

• Train on COCO with FocalNet-L with 3 focal levels:

python -m torch.distributed.launch --nproc_per_node={ngpus} main.py --config_file config/DINO/DINO_4scale_focalnet_fl3.py --coco_path {coco_path} --output_dir {output_dir}

• Train on COCO with 5scale DINO and FocalNet-L with 4 focal levels:

python -m torch.distributed.launch --nproc_per_node={ngpus} main.py --config_file config/DINO/DINO_5scale_focalnet_fl4.py --coco_path {coco_path} --output_dir {output_dir}

Model Zoos

FocalNet-DINO pretrained with Object365:

Backbone	Method	Pretrained Data	COCO minival mAP (w/o tta)	Download
Swin-L	DINO	Object365	63.1	-
FocalNet-L	DINO	Object365	63.5	in21k ckpt/o365 ckpt/coco ckpt

Related Links

Thanks to the authors of DINO, the DINO models trained with FocalNets as the backbones can be found here:

FocalNet-L + DINO: DINO + FocalNet-L

All pretrained models on imagenet-1k or imagenet-21k are provided in:

Focal Modulation Networks: Focal Modulation Networks Model Zoo.

Citation

If you find this repo useful to your project, please consider to cite it with following bib:

@misc{yang2022focalnet,  
  author = {Yang, Jianwei and Li, Chunyuan and Dai, Xiyang and Yuan, Lu and Gao, Jianfeng},
  title = {Focal Modulation Networks},
  publisher = {arXiv},
  year = {2022},
}

and also:

@misc{zhang2022dino,
      title={DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection}, 
      author={Hao Zhang and Feng Li and Shilong Liu and Lei Zhang and Hang Su and Jun Zhu and Lionel M. Ni and Heung-Yeung Shum},
      year={2022},
      eprint={2203.03605},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}