DetHub is an open source object detection / instance segmentation experiments hub. Our main contribution is supporting detection datasets and share baselines.
- Support more and more datasets
- Provide reproducible baseline configs for these datasets
- Provide pretrained models, results and inference codes for these datasets
Documentation: docs
- COCO
- Wadhwani AI Bollworm Counting Challenge(Zindi)
- CrowdHuman
- FindFallenPeople Computer Vision Project
- TensorFlow - Help Protect the Great Barrier Reef (Kaggle)
- Le2i Computer Vision Project
- LIVECell dataset
- LVIS
- Open Images Dataset
- Oxford Pets Computer Vision Project
- PACO: Parts and Attributes of Common Objects
- Sartorius - Cell Instance Segmentation (Kaggle)
- Smoke100 Computer Vision Project
- Vehicle Detection in Multi-Resolution Images (Solafune)
Please refer to get_started.md for get started. Other tutorials for:
We appreciate all contributions to improve dethub. Please refer to CONTRIBUTING.md for the contributing guideline.
This project is released under the Apache 2.0 license.
This repo borrows the architecture design and part of the code from mmdetection.
Also, please check the following openmmlab projects and the corresponding Documentation.
- OpenMMLab
- MMCV: OpenMMLab foundational library for computer vision.
- MIM: MIM Installs OpenMMLab Packages.
- MMClassification: OpenMMLab image classification toolbox and benchmark.
@article{mmdetection,
title = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark},
author = {Chen, Kai and Wang, Jiaqi and Pang, Jiangmiao and Cao, Yuhang and
Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and
Liu, Ziwei and Xu, Jiarui and Zhang, Zheng and Cheng, Dazhi and
Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and
Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong
and Shi, Jianping and Ouyang, Wanli and Loy, Chen Change and Lin, Dahua},
journal= {arXiv preprint arXiv:1906.07155},
year={2019}
}