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https://github.com/airockchip/yolov6

YOLOv6: a single-stage object detection framework dedicated to industrial applications.

https://github.com/airockchip/yolov6

Science Score: 10.0%

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    Links to: arxiv.org
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    Low similarity (9.6%) to scientific vocabulary
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Repository

YOLOv6: a single-stage object detection framework dedicated to industrial applications.

Basic Info
  • Host: GitHub
  • Owner: airockchip
  • License: gpl-3.0
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 38.6 MB
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Fork of meituan/YOLOv6
Created about 3 years ago · Last pushed almost 3 years ago
Metadata Files
Readme License

README.md

Get model optimized for RKNN

Exports model with optimization for RKNN, please refer here RKOPT_README.md

This optimization only affects the export of the model and does not affect the training process. For the training steps, please refer to the YOLOv6 official documentation.

English | 简体中文


Open In Colab Open In Kaggle

YOLOv6

Implementation of paper: - YOLOv6 v3.0: A Full-Scale Reloading 🔥 - YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications

What's New

  • [2023.03.10] Release YOLOv6-Face. 🔥 Performance
  • [2023.03.02] Update base models to version 3.0.
  • [2023.01.06] Release P6 models and enhance the performance of P5 models. ⭐️ Benchmark
    • Renew the neck of the detector with a BiC module and SimCSPSPPF Block.
    • Propose an anchor-aided training (AAT) strategy.
    • Involve a new self-distillation strategy for small models of YOLOv6.
    • Expand YOLOv6 and hit a new SOTA performance on the COCO dataset.
  • [2022.11.04] Release base models to simplify the training and deployment process.
  • [2022.09.06] Customized quantization methods. 🚀 Quantization Tutorial
  • [2022.09.05] Release M/L models and update N/T/S models with enhanced performance.
  • [2022.06.23] Release N/T/S models with excellent performance.

Benchmark

| Model | Size | mAPval
0.5:0.95 | SpeedT4
trt fp16 b1
(fps) | SpeedT4
trt fp16 b32
(fps) | Params
(M) | FLOPs
(G) | | :----------------------------------------------------------- | ---- | :----------------------- | --------------------------------------- | ---------------------------------------- | -------------------- | ------------------- | | YOLOv6-N | 640 | 37.5 | 779 | 1187 | 4.7 | 11.4 | | YOLOv6-S | 640 | 45.0 | 339 | 484 | 18.5 | 45.3 | | YOLOv6-M | 640 | 50.0 | 175 | 226 | 34.9 | 85.8 | | YOLOv6-L | 640 | 52.8 | 98 | 116 | 59.6 | 150.7 | | | | | | | | YOLOv6-N6 | 1280 | 44.9 | 228 | 281 | 10.4 | 49.8 | | YOLOv6-S6 | 1280 | 50.3 | 98 | 108 | 41.4 | 198.0 | | YOLOv6-M6 | 1280 | 55.2 | 47 | 55 | 79.6 | 379.5 | | YOLOv6-L6 | 1280 | 57.2 | 26 | 29 | 140.4 | 673.4 |

Table Notes

  • All checkpoints are trained with self-distillation except for YOLOv6-N6/S6 models trained to 300 epochs without distillation.
  • Results of the mAP and speed are evaluated on COCO val2017 dataset with the input resolution of 640×640 for P5 models and 1280x1280 for P6 models.
  • Speed is tested with TensorRT 7.2 on T4.
  • Refer to Test speed tutorial to reproduce the speed results of YOLOv6.
  • Params and FLOPs of YOLOv6 are estimated on deployed models.
Legacy models | Model | Size | mAPval
0.5:0.95 | SpeedT4
trt fp16 b1
(fps) | SpeedT4
trt fp16 b32
(fps) | Params
(M) | FLOPs
(G) | | :----------------------------------------------------------- | ---- | :------------------------------------ | --------------------------------------- | ---------------------------------------- | -------------------- | ------------------- | | [**YOLOv6-N**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6n.pt) | 640 | 35.9300e
36.3400e | 802 | 1234 | 4.3 | 11.1 | | [**YOLOv6-T**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6t.pt) | 640 | 40.3300e
41.1400e | 449 | 659 | 15.0 | 36.7 | | [**YOLOv6-S**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6s.pt) | 640 | 43.5300e
43.8400e | 358 | 495 | 17.2 | 44.2 | | [**YOLOv6-M**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6m.pt) | 640 | 49.5 | 179 | 233 | 34.3 | 82.2 | | [**YOLOv6-L-ReLU**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6l_relu.pt) | 640 | 51.7 | 113 | 149 | 58.5 | 144.0 | | [**YOLOv6-L**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6l.pt) | 640 | 52.5 | 98 | 121 | 58.5 | 144.0 | - Speed is tested with TensorRT 7.2 on T4. ### Quantized model 🚀 | Model | Size | Precision | mAPval
0.5:0.95 | SpeedT4
trt b1
(fps) | SpeedT4
trt b32
(fps) | | :-------------------- | ---- | --------- | :----------------------- | ---------------------------------- | ----------------------------------- | | **YOLOv6-N RepOpt** | 640 | INT8 | 34.8 | 1114 | 1828 | | **YOLOv6-N** | 640 | FP16 | 35.9 | 802 | 1234 | | **YOLOv6-T RepOpt** | 640 | INT8 | 39.8 | 741 | 1167 | | **YOLOv6-T** | 640 | FP16 | 40.3 | 449 | 659 | | **YOLOv6-S RepOpt** | 640 | INT8 | 43.3 | 619 | 924 | | **YOLOv6-S** | 640 | FP16 | 43.5 | 377 | 541 | - Speed is tested with TensorRT 8.4 on T4. - Precision is figured on models for 300 epochs.

Quick Start

Install ```shell git clone https://github.com/meituan/YOLOv6 cd YOLOv6 pip install -r requirements.txt ```
Reproduce our results on COCO Please refer to [Train COCO Dataset](./docs/Train_coco_data.md).
Finetune on custom data Single GPU ```shell # P5 models python tools/train.py --batch 32 --conf configs/yolov6s_finetune.py --data data/dataset.yaml --fuse_ab --device 0 # P6 models python tools/train.py --batch 32 --conf configs/yolov6s6_finetune.py --data data/dataset.yaml --img 1280 --device 0 ``` Multi GPUs (DDP mode recommended) ```shell # P5 models python -m torch.distributed.launch --nproc_per_node 8 tools/train.py --batch 256 --conf configs/yolov6s_finetune.py --data data/dataset.yaml --fuse_ab --device 0,1,2,3,4,5,6,7 # P6 models python -m torch.distributed.launch --nproc_per_node 8 tools/train.py --batch 128 --conf configs/yolov6s6_finetune.py --data data/dataset.yaml --img 1280 --device 0,1,2,3,4,5,6,7 ``` - fuse_ab: add anchor-based auxiliary branch and use Anchor Aided Training Mode (Not supported on P6 models currently) - conf: select config file to specify network/optimizer/hyperparameters. We recommend to apply yolov6n/s/m/l_finetune.py when training on your custom dataset. - data: prepare dataset and specify dataset paths in data.yaml ( [COCO](http://cocodataset.org), [YOLO format coco labels](https://github.com/meituan/YOLOv6/releases/download/0.1.0/coco2017labels.zip) ) - make sure your dataset structure as follows: ``` ├── coco │ ├── annotations │ │ ├── instances_train2017.json │ │ └── instances_val2017.json │ ├── images │ │ ├── train2017 │ │ └── val2017 │ ├── labels │ │ ├── train2017 │ │ ├── val2017 │ ├── LICENSE │ ├── README.txt ```
Resume training If your training process is corrupted, you can resume training by ``` # single GPU training. python tools/train.py --resume # multi GPU training. python -m torch.distributed.launch --nproc_per_node 8 tools/train.py --resume ``` Above command will automatically find the latest checkpoint in YOLOv6 directory, then resume the training process. Your can also specify a checkpoint path to `--resume` parameter by ``` # remember to replace /path/to/your/checkpoint/path to the checkpoint path which you want to resume training. --resume /path/to/your/checkpoint/path ``` This will resume from the specific checkpoint you provide.
Evaluation Reproduce mAP on COCO val2017 dataset with 640×640 or 1280x1280 resolution ```shell # P5 models python tools/eval.py --data data/coco.yaml --batch 32 --weights yolov6s.pt --task val --reproduce_640_eval # P6 models python tools/eval.py --data data/coco.yaml --batch 32 --weights yolov6s6.pt --task val --reproduce_640_eval --img 1280 ``` - verbose: set True to print mAP of each classes. - do_coco_metric: set True / False to enable / disable pycocotools evaluation method. - do_pr_metric: set True / False to print or not to print the precision and recall metrics. - config-file: specify a config file to define all the eval params, for example: [yolov6n_with_eval_params.py](configs/experiment/yolov6n_with_eval_params.py)
Inference First, download a pretrained model from the YOLOv6 [release](https://github.com/meituan/YOLOv6/releases/tag/0.3.0) or use your trained model to do inference. Second, run inference with `tools/infer.py` ```shell # P5 models python tools/infer.py --weights yolov6s.pt --source img.jpg / imgdir / video.mp4 # P6 models python tools/infer.py --weights yolov6s6.pt --img 1280 1280 --source img.jpg / imgdir / video.mp4 ``` If you want to inference on local camera or web camera, you can run: ```shell # P5 models python tools/infer.py --weights yolov6s.pt --webcam --webcam-addr 0 # P6 models python tools/infer.py --weights yolov6s6.pt --img 1280 1280 --webcam --webcam-addr 0 ``` `webcam-addr` can be local camera number id or rtsp address.
Deployment * [ONNX](./deploy/ONNX) * [OpenCV Python/C++](./deploy/ONNX/OpenCV) * [OpenVINO](./deploy/OpenVINO) * [TensorRT](./deploy/TensorRT)
Tutorials * [User Guide(zh_CN)](https://yolov6-docs.readthedocs.io/zh_CN/latest/) * [Train COCO Dataset](./docs/Train_coco_data.md) * [Train custom data](./docs/Train_custom_data.md) * [Test speed](./docs/Test_speed.md) * [Tutorial of Quantization for YOLOv6](./docs/Tutorial%20of%20Quantization.md)
Third-party resources * YOLOv6 NCNN Android app demo: [ncnn-android-yolov6](https://github.com/FeiGeChuanShu/ncnn-android-yolov6) from [FeiGeChuanShu](https://github.com/FeiGeChuanShu) * YOLOv6 ONNXRuntime/MNN/TNN C++: [YOLOv6-ORT](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/ort/cv/yolov6.cpp), [YOLOv6-MNN](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/mnn/cv/mnn_yolov6.cpp) and [YOLOv6-TNN](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/tnn/cv/tnn_yolov6.cpp) from [DefTruth](https://github.com/DefTruth) * YOLOv6 TensorRT Python: [yolov6-tensorrt-python](https://github.com/Linaom1214/TensorRT-For-YOLO-Series) from [Linaom1214](https://github.com/Linaom1214) * YOLOv6 TensorRT Windows C++: [yolort](https://github.com/zhiqwang/yolov5-rt-stack/tree/main/deployment/tensorrt-yolov6) from [Wei Zeng](https://github.com/Wulingtian) * [YOLOv6 web demo](https://huggingface.co/spaces/nateraw/yolov6) on [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/nateraw/yolov6) * [Interactive demo](https://yolov6.dagshubusercontent.com/) on [DagsHub](https://dagshub.com) with [Streamlit](https://github.com/streamlit/streamlit) * Tutorial: [How to train YOLOv6 on a custom dataset](https://blog.roboflow.com/how-to-train-yolov6-on-a-custom-dataset/) Open In Colab * YouTube Tutorial: [How to train YOLOv6 on a custom dataset](https://youtu.be/fFCWrMFH2UY) * Demo of YOLOv6 inference on Google Colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mahdilamb/YOLOv6/blob/main/inference.ipynb) * Blog post: [YOLOv6 Object Detection – Paper Explanation and Inference](https://learnopencv.com/yolov6-object-detection/)

FAQ（Continuously updated）

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Dependencies

requirements.txt pypi
  • PyYAML >=5.3.1
  • addict >=2.4.0
  • numpy >=1.24.0
  • onnx >=1.10.0
  • onnx-simplifier >=0.3.6
  • opencv-python >=4.1.2
  • pycocotools >=2.0
  • scipy >=1.4.1
  • tensorboard >=2.7.0
  • thop *
  • torch >=1.8.0
  • torchvision >=0.9.0
  • tqdm >=4.41.0
tools/quantization/tensorrt/requirements.txt pypi
  • nvidia-pyindex *
  • pycuda ==2020.1
  • pytorch-quantization *
  • tensorrt *