> Each sub-image denotes a task. All images are from search engine. ## Table of Contents - [Changelog](#changelog) - [Install](#install) - [Usage](#usage) - [Get started](#get-start) - [Step-by-step tutorial](#tutorials) - [Model Zoo](#model-zoo) - [Contributing](#contributing) - [License](#license) - [Citation](#citation) - [Acknowledgement](#acknowledgement) ## Changelog * Dec. 03, 2020: cvpods v0.1 released. ## Install ### Requirements * Linux with Python ≥ 3.6 * PyTorch ≥ 1.3 and torchvision that matches the PyTorch installation. You can install them together at pytorch.org to make sure of this * OpenCV is optional and needed by demo and visualization ### Build cvpods from source **Make sure GPU is available on your local machine.** ```shell # Install cvpods with GPU directly pip install 'git+https://github.com/Megvii-BaseDetection/cvpods.git' --user # Or, to install it with GPU from a local clone: git clone https://github.com/Megvii-BaseDetection/cvpods.git pip install -e cvpods --user # Or, to build it without GPU from a local clone: FORCE_CUDA=1 pip install -e cvpods --user ``` ## Usage Here we demonstrate the basic usage of cvpods (Inference & Train). For more features of cvpods, please refer to our documentation or provided tutorials. ### Get Start Here we use coco object detection task as an example. ``` # Preprare data path ln -s /path/to/your/coco/dataset datasets/coco # Enter a specific experiment dir cd playground/retinanet/retinanet.res50.fpn.coco.multiscale.1x # Train pods_train --num-gpus 8 # Test pods_test --num-gpus 8 \ MODEL.WEIGHTS /path/to/your/save_dir/ckpt.pth # optional OUTPUT_DIR /path/to/your/save_dir # optional # Multi node training ## sudo apt install net-tools ifconfig pods_train --num-gpus 8 --num-machines N --machine-rank 0/1/.../N-1 --dist-url "tcp://MASTER_IP:port" ``` ### Tutorials We provide a detailed tutorial, which covers introduction, usage, and extend guides in [cvpods_tutorials](https://github.com/Megvii-BaseDetection/cvpods/blob/master/docs/tutorials/cvpods%20tutorials.ipynb). For all API usages, please refer to our [documentation](https://cvpods.readthedocs.io/). ## Model ZOO For all the models supported by cvpods, please refer to [MODEL_ZOO](https://github.com/Megvii-BaseDetection/cvpods/blob/master/playground/README.md). We provide 50+ methods across ~15 dataset and ~10 computer vision tasks. cvpods has also supported many research projects of MEGVII Research. ### Projects based on cvpods > List is sorted by names. * [AutoAssign](https://github.com/Megvii-BaseDetection/AutoAssign) * [BorderDet](https://github.com/Megvii-BaseDetection/BorderDet) * [DeFCN](https://github.com/Megvii-BaseDetection/DeFCN) * [DisAlign](https://github.com/Megvii-BaseDetection/DisAlign) * [DynamicHead](https://github.com/StevenGrove/DynamicHead) * [DynamicRouting](https://github.com/Megvii-BaseDetection/DynamicRouting) * [LearnableTreeFilterV2](https://github.com/StevenGrove/LearnableTreeFilterV2) * [LLA](https://github.com/Megvii-BaseDetection/LLA) * [OTA](https://github.com/Megvii-BaseDetection/OTA) * [SelfSup](https://github.com/poodarchu/SelfSup) * [YOLOF](https://github.com/megvii-model/YOLOF) ## Contributing Any kind of contributions (new models / bug report / typo / docs) are welcomed. Please refer to [CONTRIBUTING](CONTRIBUTING.md) for more details. ## License [Apache v2](LICENSE) © Base Detection ## Acknowledgement and special thanks cvpods adopts many components (e.g. network layers) of Detectron2, while cvpods has many advantanges in task support, speed, usability, etc. For more details about official detectron2, please check [DETECTRON2](https://github.com/facebookresearch/detectron2/blob/master/README.md) ## Citing cvpods If you are using cvpods in your research or wish to refer to the baseline results published in this repo, please use the following BibTeX entry. ```BibTeX @misc{zhu2020cvpods, title={cvpods: All-in-one Toolbox for Computer Vision Research}, author={Zhu*, Benjin and Wang*, Feng and Wang, Jianfeng and Yang, Siwei and Chen, Jianhu and Li, Zeming}, year={2020} } ```