FemtoDet

Official codes of ICCV2023 paper: <<Femtodet: an object detection baseline for energy versus performance tradeoffs>>

Dependencies

Python 3.8
Torch 1.9.1+cu111
Torchvision 0.10.1+cu111
mmcv-full 1.4.2
mmdet 2.23.0

Installation

Do it as mmdetection had done.

Preparation

Download the dataset.

We mainly train FemtoDet on Pascal VOC 0712, you should firstly download the datasets. By default, we assume the dataset is stored in ./data/.
Dataset preparation.

Then, you can move all images to ./data/voc2coco/jpeg/;you can use our converted coco format annotation files(umbz) and put these files to ./data/voc2coco/annotations/; finally, the directory structure is

*data/voc2coco
    *jpeg
        *2008_003841.jpg
        *...
    *annotations
        *trainvoc_annotations.json
        *testvoc_annotations.json

Download the initialized models. We trained our designed backbone on ImageNet 1k, and used it for the inite weights(6tns) of FemtoDet.

FemtoDet/weights/*

Training

bash ./tools/train_femtodet.sh 4

Results (trained on VOC) and Models

trained model and logs download (x38z)

|  Detector  | Params | box AP50 |              Config                    | 
---------------------------------------------------------------------------
|            |        |   37.1   | ./configs/femtoDet/femtodet_0stage.py  |
                      -----------------------------------------------------
|  FemtoDet  | 68.77k |   40.4   | ./configs/femtoDet/femtodet_1stage.py  |
                      -----------------------------------------------------
|            |        |   44.4   | ./configs/femtoDet/femtodet_2stage.py  |
                      -----------------------------------------------------
|            |        |   46.5   | ./configs/femtoDet/femtodet_3stage.py  |
---------------------------------------------------------------------------

References

If you find the code useful for your research, please consider citing:

@misc{tu2023femtodet,
      title={FemtoDet: An Object Detection Baseline for Energy Versus Performance Tradeoffs}, 
      author={Peng Tu and Xu Xie and Guo AI and Yuexiang Li and Yawen Huang and Yefeng Zheng},
      year={2023},
      eprint={2301.06719},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}