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femtodet-v3

The unofficial implementation of FemtoDet

https://github.com/zs-yang/femtodet-v3

Science Score: 54.0%

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    Found CITATION.cff file
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    Links to: arxiv.org
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    Scientific vocabulary similarity
    Low similarity (7.5%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

The unofficial implementation of FemtoDet

Basic Info
  • Host: GitHub
  • Owner: ZS-YANG
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 21.2 MB
Statistics
  • Stars: 4
  • Watchers: 1
  • Forks: 0
  • Open Issues: 2
  • Releases: 0
Created about 2 years ago · Last pushed about 2 years ago
Metadata Files
Readme License Citation

README.md

FemtoDet-v3

FemtoDet的非官方实现

paper:Femtodet: an object detection baseline for energy versus performance tradeoffs

写在前面

原作者代码基于 mmdet2.x，由于mmdet3.x进行了大量更新，原始代码不能直接应用于mmdet3.0的项目中，因此基于mmdet3.2.0对原始代码进行了重构 初始代码基于

To list

  • [ ] 发布在VOC0712数据集上的训练结果
  • [ ] 对代码进行进一步重构解耦
  • [ ] 更新教程文档
  • [X] 精度对齐(使用femtodet0stageyolox.py 的训练结果比femtodet_0stage.py高很多，需要找到原因)

Dependencies and Installation

按照mmdetection的要求安装torch 和 mmcv

之后克隆本项目，进行mmdet的安装

commandline git clone https://github.com/ZS-YANG/FemtoDet-v3.git cd FemtoDet-v3 pip install .

Preparation

  1. 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/.

  1. Dataset preparation. 使用pascal_voc.py 转换数据集格式，转换完成后配置femtoDet中的数据集路径

  2. Download the initialized models.

We trained our designed backbone on ImageNet 1k, and used it for the inite weights)(hx8k) of FemtoDet.

Results (trained on VOC) and Models

原始作者训练精度

| Detector | Params | box AP50 | Config | |----------|--------|----------|---------------------------------------| | | | 37.1 | ./configs/femtoDet/femtodet0stage.py | | FemtoDet | 68.77k | 40.4 | ./configs/femtoDet/femtodet1stage.py | | | | 44.4 | ./configs/femtoDet/femtodet2stage.py | | | | 46.5 | ./configs/femtoDet/femtodet3stage.py |

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} }

Owner

  • Login: ZS-YANG
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - name: "MMDetection Contributors"
title: "OpenMMLab Detection Toolbox and Benchmark"
date-released: 2018-08-22
url: "https://github.com/open-mmlab/mmdetection"
license: Apache-2.0

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Dependencies

docker/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/serve/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/serve_cn/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
requirements/albu.txt pypi
  • albumentations >=0.3.2
requirements/build.txt pypi
  • cython *
  • numpy *
requirements/docs.txt pypi
  • docutils ==0.16.0
  • myst-parser *
  • sphinx ==4.0.2
  • sphinx-copybutton *
  • sphinx_markdown_tables *
  • sphinx_rtd_theme ==0.5.2
  • urllib3 <2.0.0
requirements/mminstall.txt pypi
  • mmcv >=2.0.0rc4,<2.2.0
  • mmengine >=0.7.1,<1.0.0
requirements/multimodal.txt pypi
  • fairscale *
  • nltk *
  • pycocoevalcap *
  • transformers *
requirements/optional.txt pypi
  • cityscapesscripts *
  • fairscale *
  • imagecorruptions *
  • scikit-learn *
requirements/readthedocs.txt pypi
  • mmcv >=2.0.0rc4,<2.2.0
  • mmengine >=0.7.1,<1.0.0
  • scipy *
  • torch *
  • torchvision *
  • urllib3 <2.0.0
requirements/runtime.txt pypi
  • matplotlib *
  • numpy *
  • pycocotools *
  • scipy *
  • shapely *
  • six *
  • terminaltables *
  • tqdm *
requirements/tests.txt pypi
  • asynctest * test
  • cityscapesscripts * test
  • codecov * test
  • flake8 * test
  • imagecorruptions * test
  • instaboostfast * test
  • interrogate * test
  • isort ==4.3.21 test
  • kwarray * test
  • memory_profiler * test
  • nltk * test
  • onnx ==1.7.0 test
  • onnxruntime >=1.8.0 test
  • parameterized * test
  • prettytable * test
  • protobuf <=3.20.1 test
  • psutil * test
  • pytest * test
  • transformers * test
  • ubelt * test
  • xdoctest >=0.10.0 test
  • yapf * test
requirements/tracking.txt pypi
  • mmpretrain *
  • motmetrics *
  • numpy <1.24.0
  • scikit-learn *
  • seaborn *
requirements.txt pypi
setup.py pypi