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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
All-in-one Toolbox for Computer Vision Research.
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<div align=center><img src=".github/cvpods_logo.png" width="400" ><div align=left>
[](https://github.com/Megvii-BaseDetection/cvpods)
Welcome to **cvpods**, a versatile and efficient codebase for many computer vision tasks: classification, segmentation, detection, self-supervised learning, keypoints and 3D(classification / segmentation / detection / representation learing), etc. The aim of cvpods is to achieve efficient experiments management and smooth tasks-switching.
<div align=center><img src=".github/cvpods_tasks.png"><div align=left>
> 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 rExcerpt of 4,865 characters
Read on GitHub
23
Feng Wang · @Megvii-BaseDetection · China
18
Songyang Zhang · Shanghai AI Lab · China
4
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2bb8ead745c2b271, topic:computer-vision, desc:computer vision, readme:computer vision
matched fp:2bb8ead745c2b271, topic:pytorch