This is the official implementation of the paper "Less is More: Focus Attention for Efficient DETR"

Authors: Dehua Zheng, Wenhui Dong, Hailin Hu, Xinghao Chen, Yunhe Wang.

[arXiv] [BibTeX]

Focus-DETR is a model that focuses attention on more informative tokens for a better trade-off between computation efficiency and model accuracy. Compared with the state-of-the-art sparse transformed-based detector under the same setting, our Focus-DETR gets comparable complexity while achieving 50.4AP (+2.2) on COCO.

• Focus-DETR
• Table of Contents
• Main Results with Pretrained Models - Pretrained focus_detr with ResNet Backbone
  • Pretrained focus_detr with Swin-Transformer Backbone
• Installation
• Training
• Evaluation
• Citing Focus-DETR

Here we provide the pretrained Focus-DETR weights based on detrex.

Note:

• Swin-X-384 means the backbone pretrained resolution is 384 x 384 and IN22k to In1k means the model is pretrained on ImageNet-22k and finetuned on ImageNet-1k.

Please refer to Installation Instructions for the details of installation.

All configs can be trained with:

cd detrex
python tools/train_net.py --config-file projects/focus_detr/configs/path/to/config.py --num-gpus 8

By default, we use 8 GPUs with total batch size as 16 for training.

Model evaluation can be done as follows:

cd detrex
python tools/train_net.py --config-file projects/focus_detr/configs/path/to/config.py --eval-only train.init_checkpoint=/path/to/model_checkpoint

If you find our work helpful for your research, please consider citing the following BibTeX entry.

@misc{zheng2023more,
      title={Less is More: Focus Attention for Efficient DETR}, 
      author={Dehua Zheng and Wenhui Dong and Hailin Hu and Xinghao Chen and Yunhe Wang},
      year={2023},
      eprint={2307.12612},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}