Metadata-Version: 2.1
Name: yolox
Version: 0.3.0
Summary: UNKNOWN
Home-page: https://github.com/Megvii-BaseDetection/YOLOX
Author: megvii basedet team
License: UNKNOWN
Project-URL: Documentation, https://yolox.readthedocs.io
Project-URL: Source, https://github.com/Megvii-BaseDetection/YOLOX
Project-URL: Tracker, https://github.com/Megvii-BaseDetection/YOLOX/issues
Description: <div align="center"><img src="assets/logo.png" width="350"></div>
        <img src="assets/demo.png" >
        
        ## Introduction
        YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities.
        For more details, please refer to our [report on Arxiv](https://arxiv.org/abs/2107.08430).
        
        This repo is an implementation of PyTorch version YOLOX, there is also a [MegEngine implementation](https://github.com/MegEngine/YOLOX).
        
        <img src="assets/git_fig.png" width="1000" >
        
        ## Updates!!
        * 【2023/02/28】 We support assignment visualization tool, see doc [here](./docs/assignment_visualization.md).
        * 【2022/04/14】 We support jit compile op.
        * 【2021/08/19】 We optimize the training process with **2x** faster training and **~1%** higher performance! See [notes](docs/updates_note.md) for more details.
        * 【2021/08/05】 We release [MegEngine version YOLOX](https://github.com/MegEngine/YOLOX).
        * 【2021/07/28】 We fix the fatal error of [memory leak](https://github.com/Megvii-BaseDetection/YOLOX/issues/103)
        * 【2021/07/26】 We now support [MegEngine](https://github.com/Megvii-BaseDetection/YOLOX/tree/main/demo/MegEngine) deployment.
        * 【2021/07/20】 We have released our technical report on [Arxiv](https://arxiv.org/abs/2107.08430).
        
        ## Coming soon
        - [ ] YOLOX-P6 and larger model.
        - [ ] Objects365 pretrain.
        - [ ] Transformer modules.
        - [ ] More features in need.
        
        ## Benchmark
        
        #### Standard Models.
        
        |Model |size |mAP<sup>val<br>0.5:0.95 |mAP<sup>test<br>0.5:0.95 | Speed V100<br>(ms) | Params<br>(M) |FLOPs<br>(G)| weights |
        | ------        |:---: | :---:    | :---:       |:---:     |:---:  | :---: | :----: |
        |[YOLOX-s](./exps/default/yolox_s.py)    |640  |40.5 |40.5      |9.8      |9.0 | 26.8 | [github](https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.pth) |
        |[YOLOX-m](./exps/default/yolox_m.py)    |640  |46.9 |47.2      |12.3     |25.3 |73.8| [github](https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_m.pth) |
        |[YOLOX-l](./exps/default/yolox_l.py)    |640  |49.7 |50.1      |14.5     |54.2| 155.6 | [github](https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_l.pth) |
        |[YOLOX-x](./exps/default/yolox_x.py)   |640   |51.1 |**51.5**  | 17.3    |99.1 |281.9 | [github](https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_x.pth) |
        |[YOLOX-Darknet53](./exps/default/yolov3.py)   |640  | 47.7 | 48.0 | 11.1 |63.7 | 185.3 | [github](https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_darknet.pth) |
        
        <details>
        <summary>Legacy models</summary>
        
        |Model |size |mAP<sup>test<br>0.5:0.95 | Speed V100<br>(ms) | Params<br>(M) |FLOPs<br>(G)| weights |
        | ------        |:---: | :---:       |:---:     |:---:  | :---: | :----: |
        |[YOLOX-s](./exps/default/yolox_s.py)    |640  |39.6      |9.8     |9.0 | 26.8 | [onedrive](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/EW62gmO2vnNNs5npxjzunVwB9p307qqygaCkXdTO88BLUg?e=NMTQYw)/[github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_s.pth) |
        |[YOLOX-m](./exps/default/yolox_m.py)    |640  |46.4      |12.3     |25.3 |73.8| [onedrive](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/ERMTP7VFqrVBrXKMU7Vl4TcBQs0SUeCT7kvc-JdIbej4tQ?e=1MDo9y)/[github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_m.pth) |
        |[YOLOX-l](./exps/default/yolox_l.py)    |640  |50.0  |14.5 |54.2| 155.6 | [onedrive](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/EWA8w_IEOzBKvuueBqfaZh0BeoG5sVzR-XYbOJO4YlOkRw?e=wHWOBE)/[github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_l.pth) |
        |[YOLOX-x](./exps/default/yolox_x.py)   |640  |**51.2**      | 17.3 |99.1 |281.9 | [onedrive](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/EdgVPHBziOVBtGAXHfeHI5kBza0q9yyueMGdT0wXZfI1rQ?e=tABO5u)/[github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_x.pth) |
        |[YOLOX-Darknet53](./exps/default/yolov3.py)   |640  | 47.4      | 11.1 |63.7 | 185.3 | [onedrive](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/EZ-MV1r_fMFPkPrNjvbJEMoBLOLAnXH-XKEB77w8LhXL6Q?e=mf6wOc)/[github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_darknet53.pth) |
        
        </details>
        
        #### Light Models.
        
        |Model |size |mAP<sup>val<br>0.5:0.95 | Params<br>(M) |FLOPs<br>(G)| weights |
        | ------        |:---:  |  :---:       |:---:     |:---:  | :---: |
        |[YOLOX-Nano](./exps/default/yolox_nano.py) |416  |25.8  | 0.91 |1.08 | [github](https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_nano.pth) |
        |[YOLOX-Tiny](./exps/default/yolox_tiny.py) |416  |32.8 | 5.06 |6.45 | [github](https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_tiny.pth) |
        
        
        <details>
        <summary>Legacy models</summary>
        
        |Model |size |mAP<sup>val<br>0.5:0.95 | Params<br>(M) |FLOPs<br>(G)| weights |
        | ------        |:---:  |  :---:       |:---:     |:---:  | :---: |
        |[YOLOX-Nano](./exps/default/yolox_nano.py) |416  |25.3  | 0.91 |1.08 | [github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_nano.pth) |
        |[YOLOX-Tiny](./exps/default/yolox_tiny.py) |416  |32.8 | 5.06 |6.45 | [github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_tiny_32dot8.pth) |
        
        </details>
        
        ## Quick Start
        
        <details>
        <summary>Installation</summary>
        
        Step1. Install YOLOX from source.
        ```shell
        git clone git@github.com:Megvii-BaseDetection/YOLOX.git
        cd YOLOX
        pip3 install -v -e .  # or  python3 setup.py develop
        ```
        
        </details>
        
        <details>
        <summary>Demo</summary>
        
        Step1. Download a pretrained model from the benchmark table.
        
        Step2. Use either -n or -f to specify your detector's config. For example:
        
        ```shell
        python tools/demo.py image -n yolox-s -c /path/to/your/yolox_s.pth --path assets/dog.jpg --conf 0.25 --nms 0.45 --tsize 640 --save_result --device [cpu/gpu]
        ```
        or
        ```shell
        python tools/demo.py image -f exps/default/yolox_s.py -c /path/to/your/yolox_s.pth --path assets/dog.jpg --conf 0.25 --nms 0.45 --tsize 640 --save_result --device [cpu/gpu]
        ```
        Demo for video:
        ```shell
        python tools/demo.py video -n yolox-s -c /path/to/your/yolox_s.pth --path /path/to/your/video --conf 0.25 --nms 0.45 --tsize 640 --save_result --device [cpu/gpu]
        ```
        
        
        </details>
        
        <details>
        <summary>Reproduce our results on COCO</summary>
        
        Step1. Prepare COCO dataset
        ```shell
        cd <YOLOX_HOME>
        ln -s /path/to/your/COCO ./datasets/COCO
        ```
        
        Step2. Reproduce our results on COCO by specifying -n:
        
        ```shell
        python -m yolox.tools.train -n yolox-s -d 8 -b 64 --fp16 -o [--cache]
                                       yolox-m
                                       yolox-l
                                       yolox-x
        ```
        * -d: number of gpu devices
        * -b: total batch size, the recommended number for -b is num-gpu * 8
        * --fp16: mixed precision training
        * --cache: caching imgs into RAM to accelarate training, which need large system RAM.
        
        
        
        When using -f, the above commands are equivalent to:
        ```shell
        python -m yolox.tools.train -f exps/default/yolox_s.py -d 8 -b 64 --fp16 -o [--cache]
                                       exps/default/yolox_m.py
                                       exps/default/yolox_l.py
                                       exps/default/yolox_x.py
        ```
        
        **Multi Machine Training**
        
        We also support multi-nodes training. Just add the following args:
        * --num\_machines: num of your total training nodes
        * --machine\_rank: specify the rank of each node
        
        Suppose you want to train YOLOX on 2 machines, and your master machines's IP is 123.123.123.123, use port 12312 and TCP.
        
        On master machine, run
        ```shell
        python tools/train.py -n yolox-s -b 128 --dist-url tcp://123.123.123.123:12312 --num_machines 2 --machine_rank 0
        ```
        On the second machine, run
        ```shell
        python tools/train.py -n yolox-s -b 128 --dist-url tcp://123.123.123.123:12312 --num_machines 2 --machine_rank 1
        ```
        
        **Logging to Weights & Biases**
        
        To log metrics, predictions and model checkpoints to [W&B](https://docs.wandb.ai/guides/integrations/other/yolox) use the command line argument `--logger wandb` and use the prefix "wandb-" to specify arguments for initializing the wandb run.
        
        ```shell
        python tools/train.py -n yolox-s -d 8 -b 64 --fp16 -o [--cache] --logger wandb wandb-project <project name>
                                 yolox-m
                                 yolox-l
                                 yolox-x
        ```
        
        An example wandb dashboard is available [here](https://wandb.ai/manan-goel/yolox-nano/runs/3pzfeom0)
        
        **Others**
        
        See more information with the following command:
        ```shell
        python -m yolox.tools.train --help
        ```
        
        </details>
        
        
        <details>
        <summary>Evaluation</summary>
        
        We support batch testing for fast evaluation:
        
        ```shell
        python -m yolox.tools.eval -n  yolox-s -c yolox_s.pth -b 64 -d 8 --conf 0.001 [--fp16] [--fuse]
                                       yolox-m
                                       yolox-l
                                       yolox-x
        ```
        * --fuse: fuse conv and bn
        * -d: number of GPUs used for evaluation. DEFAULT: All GPUs available will be used.
        * -b: total batch size across on all GPUs
        
        To reproduce speed test, we use the following command:
        ```shell
        python -m yolox.tools.eval -n  yolox-s -c yolox_s.pth -b 1 -d 1 --conf 0.001 --fp16 --fuse
                                       yolox-m
                                       yolox-l
                                       yolox-x
        ```
        
        </details>
        
        
        <details>
        <summary>Tutorials</summary>
        
        *  [Training on custom data](docs/train_custom_data.md)
        *  [Caching for custom data](docs/cache.md)
        *  [Manipulating training image size](docs/manipulate_training_image_size.md)
        *  [Assignment visualization](docs/assignment_visualization.md)
        *  [Freezing model](docs/freeze_module.md)
        
        </details>
        
        ## Deployment
        
        
        1. [MegEngine in C++ and Python](./demo/MegEngine)
        2. [ONNX export and an ONNXRuntime](./demo/ONNXRuntime)
        3. [TensorRT in C++ and Python](./demo/TensorRT)
        4. [ncnn in C++ and Java](./demo/ncnn)
        5. [OpenVINO in C++ and Python](./demo/OpenVINO)
        6. [Accelerate YOLOX inference with nebullvm in Python](./demo/nebullvm)
        
        ## Third-party resources
        * YOLOX for streaming perception: [StreamYOLO (CVPR 2022 Oral)](https://github.com/yancie-yjr/StreamYOLO)
        * The YOLOX-s and YOLOX-nano are Integrated into [ModelScope](https://www.modelscope.cn/home). Try out the Online Demo at [YOLOX-s](https://www.modelscope.cn/models/damo/cv_cspnet_image-object-detection_yolox/summary) and [YOLOX-Nano](https://www.modelscope.cn/models/damo/cv_cspnet_image-object-detection_yolox_nano_coco/summary) respectively 🚀.
        * Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/Sultannn/YOLOX-Demo)
        * The ncnn android app with video support: [ncnn-android-yolox](https://github.com/FeiGeChuanShu/ncnn-android-yolox) from [FeiGeChuanShu](https://github.com/FeiGeChuanShu)
        * YOLOX with Tengine support: [Tengine](https://github.com/OAID/Tengine/blob/tengine-lite/examples/tm_yolox.cpp) from [BUG1989](https://github.com/BUG1989)
        * YOLOX + ROS2 Foxy: [YOLOX-ROS](https://github.com/Ar-Ray-code/YOLOX-ROS) from [Ar-Ray](https://github.com/Ar-Ray-code)
        * YOLOX Deploy DeepStream: [YOLOX-deepstream](https://github.com/nanmi/YOLOX-deepstream) from [nanmi](https://github.com/nanmi)
        * YOLOX MNN/TNN/ONNXRuntime: [YOLOX-MNN](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/mnn/cv/mnn_yolox.cpp)、[YOLOX-TNN](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/tnn/cv/tnn_yolox.cpp) and [YOLOX-ONNXRuntime C++](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/ort/cv/yolox.cpp) from [DefTruth](https://github.com/DefTruth)
        * Converting darknet or yolov5 datasets to COCO format for YOLOX: [YOLO2COCO](https://github.com/RapidAI/YOLO2COCO) from [Daniel](https://github.com/znsoftm)
        
        ## Cite YOLOX
        If you use YOLOX in your research, please cite our work by using the following BibTeX entry:
        
        ```latex
         @article{yolox2021,
          title={YOLOX: Exceeding YOLO Series in 2021},
          author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},
          journal={arXiv preprint arXiv:2107.08430},
          year={2021}
        }
        ```
        ## In memory of Dr. Jian Sun
        Without the guidance of [Dr. Jian Sun](http://www.jiansun.org/), YOLOX would not have been released and open sourced to the community.
        The passing away of Dr. Jian is a huge loss to the Computer Vision field. We add this section here to express our remembrance and condolences to our captain Dr. Jian.
        It is hoped that every AI practitioner in the world will stick to the concept of "continuous innovation to expand cognitive boundaries, and extraordinary technology to achieve product value" and move forward all the way.
        
        <div align="center"><img src="assets/sunjian.png" width="200"></div>
        没有孙剑博士的指导，YOLOX也不会问世并开源给社区使用。
        孙剑博士的离去是CV领域的一大损失，我们在此特别添加了这个部分来表达对我们的“船长”孙老师的纪念和哀思。
        希望世界上的每个AI从业者秉持着“持续创新拓展认知边界，非凡科技成就产品价值”的观念，一路向前。
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.6
Description-Content-Type: text/markdown
