# FemtoDet

**Repository Path**: samallerlang/FemtoDet

## Basic Information

- **Project Name**: FemtoDet
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-06-26
- **Last Updated**: 2024-06-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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](https://github.com/open-mmlab/mmdetection/tree/v2.23.0) had done.

### Preparation
1. Download the dataset.
   
   We mainly train FemtoDet on [Pascal VOC 0712](http://host.robots.ox.ac.uk/pascal/VOC/), you should firstly download the datasets. By default, we assume the dataset is stored in ./data/.

2. Dataset preparation.
   
   Then, you can move all images to ./data/voc2coco/jpeg/*;you can use our converted coco format [annotation files](https://pan.baidu.com/s/1SLgZd_2cLhLFC54lLM3sHg?pwd=umbz)(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
```

3. Download the initialized models.
   
   We trained our designed backbone on ImageNet 1k, and used it for [the inite weights](https://pan.baidu.com/s/1JGsvlvzPkb5nxGBaRSD7ng?pwd=hx8k))(hx8k) of FemtoDet.

```
FemtoDet/weights/*
```

### Training
```
bash ./tools/train_femtodet.sh 4
```

### Results (trained on VOC) and Models

[trained model and logs download](https://pan.baidu.com/s/1IBpYyhhGA-kg71S_1OJpjA?pwd=7aok) (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  |
---------------------------------------------------------------------------
```

### Links of [Deployment (exploring to onnx)](https://github.com/xiexu666/FemtoDet_deploy) and [FemtoDet in mmdet3](https://github.com/xiexu666/FemtoDet_mmdet3) versions.

### References
If you find the code useful for your research, please consider citing:
```bib
@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}
}
```