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)(hx8k) of FemtoDet.
FemtoDet/weights/*
Training
bash ./tools/train_femtodet.sh 4
Results (trained on VOC) and Models
trained model and logs download (7aok)
| 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:
@InProceedings{Tu_2023_ICCV,
author = {Tu, Peng and Xie, Xu and Ai, Guo and Li, Yuexiang and Huang, Yawen and Zheng, Yefeng},
title = {FemtoDet: An Object Detection Baseline for Energy Versus Performance Tradeoffs},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2023},
pages = {13318-13327}
}
@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}
}