- Release [PaddleYOLO](https://github.com/PaddlePaddle/PaddleYOLO) which overs classic and latest models of [YOLO family](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/docs/MODEL_ZOO_en.md): YOLOv3, PP-YOLOE (a real-time high-precision object detection model developed by Baidu PaddlePaddle), and cutting-edge detection algorithms such as YOLOv4, YOLOv5, YOLOX, YOLOv6, and YOLOv7
- Release [PaddleYOLO](https://github.com/PaddlePaddle/PaddleYOLO) which overs classic and latest models of [YOLO family](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/docs/MODEL_ZOO_en.md): YOLOv3, PP-YOLOE (a real-time high-precision object detection model developed by Baidu PaddlePaddle), and cutting-edge detection algorithms such as YOLOv4, YOLOv5, YOLOX, YOLOv6, YOLOv7 and YOLOv8
- Newly add high precision detection model based on [ViT](configs/vitdet) backbone network, with a 55.7% mAP accuracy on COCO dataset; newly add multi-object tracking model [OC-SORT](configs/mot/ocsort); newly add [ConvNeXt](configs/convnext) backbone network.
- All models were trained and tested in the COCO17 dataset.
- The codes of [YOLOv5](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov5),[YOLOv6](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov6) and [YOLOv7](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov7) can be found in [PaddleYOLO](https://github.com/PaddlePaddle/PaddleYOLO). Note that **the LICENSE of PaddleYOLO is GPL 3.0**.
- The codes of [YOLOv5](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov5),[YOLOv6](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov6),[YOLOv7](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov7) and [YOLOv8](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov8) can be found in [PaddleYOLO](https://github.com/PaddlePaddle/PaddleYOLO). Note that **the LICENSE of PaddleYOLO is GPL 3.0**.
- Unless special instructions, all the ResNet backbone network using [ResNet-B](https://arxiv.org/pdf/1812.01187) structure.
-**Inference time (FPS)**: The reasoning time was calculated on a Tesla V100 GPU by `tools/eval.py` testing all validation sets in FPS (number of pictures/second). CuDNN version is 7.5, including data loading, network forward execution and post-processing, and Batch size is 1.
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@@ -166,7 +166,7 @@ Please refer to [Model Zoo for PaddleYOLO](https://github.com/PaddlePaddle/Paddl
Please refer to [YOLOv5](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov5)
### YOLOv6
### YOLOv6(v3.0)
Please refer to [YOLOv6](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov6)
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@@ -174,6 +174,10 @@ Please refer to [YOLOv6](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop
Please refer to [YOLOv7](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov7)
### YOLOv8
Please refer to [YOLOv7](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov8)
### RTMDet
Please refer to [RTMDet](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/rtmdet)
**PaddleYOLO** is a YOLO Series toolbox based on [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection), **only relevant codes of YOLO series models are included**. It supports `YOLOv3`,`PP-YOLO`,`PP-YOLOv2`,`PP-YOLOE`,`PP-YOLOE+`,`YOLOX`,`YOLOv5`,`YOLOv6`,`YOLOv7`,`RTMDet` and so on. Welcome to use and build it together!
**PaddleYOLO** is a YOLO Series toolbox based on [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection), **only relevant codes of YOLO series models are included**. It supports `YOLOv3`,`PP-YOLO`,`PP-YOLOv2`,`PP-YOLOE`,`PP-YOLOE+`,`YOLOX`,`YOLOv5`,`YOLOv6`,`YOLOv7`,`YOLOv8`,`RTMDet` and so on. Welcome to use and build it together!
## Updates
* 【2023/01/10】Support [YOLOv8](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8) inference and deploy;
* 【2022/09/29】Support [RTMDet](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/rtmdet) inference and deploy;
* 【2022/09/19】Support the new version of [`YOLOv6`](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6), including n/t/s/m/l model;
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@@ -29,13 +31,13 @@
**Notes：**
- The Licence of **PaddleYOLO** is **GPL 3.0**, the codes of [YOLOv5](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5),[YOLOv7](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov7) and [YOLOv6](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6) will not be merged into [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection). Except for these three YOLO models, other YOLO models are recommended to use in [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection), **which will be the first to release the latest progress of PP-YOLO series detection model**;
- The Licence of **PaddleYOLO** is **GPL 3.0**, the codes of [YOLOv5](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5),[YOLOv6](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6),[YOLOv7](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov7) and [YOLOv8](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8) will not be merged into [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection). Except for these three YOLO models, other YOLO models are recommended to use in [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection), **which will be the first to release the latest progress of PP-YOLO series detection model**;
- To use **PaddleYOLO**, **PaddlePaddle-2.3.2 or above is recommended**，please refer to the [official website](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html) to download the appropriate version. **For Windows platforms, please install the paddle develop version**;
- Training **Custom dataset** please refer to [doc](#CustomDataset) and [issue](https://github.com/PaddlePaddle/PaddleYOLO/issues/43). Please **ensure COCO trained weights are loaded as pre-train** at first. We recommend to use YOLO detection model **with a total `batch_size` at least greater than `64` to train**. If the resources are insufficient, please **use the smaller model** or **reduce the input size of the model**. To ensure high detection accuracy, **you'd better never try to using single GPU or total `batch_size` less than `32` for training**;
