Update README.md (#13114)
* Update README.md Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Auto-format by https://ultralytics.com/actions * Update README.zh-CN.md Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> --------- Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>pull/13115/head
Glenn Jocher
GitHub
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@ -185,7 +185,7 @@ python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml -
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<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" /></a>
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</div>
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| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW |
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| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW |
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| :--------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
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| Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLOv5 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet2) lets you save YOLOv5 models, resume training, and interactively visualise and debug predictions | Run YOLOv5 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |
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@ -230,7 +230,7 @@ YOLOv5 has been designed to be super easy to get started and simple to learn. We
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| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
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| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
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| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
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| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)<br>+ [TTA] | 1280<br>1536 | 55.0<br>**55.8** | 72.7<br>**72.7** | 3136<br>- | 26.2<br>- | 19.4<br>- | 140.7<br>- | 209.8<br>- |
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| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)<br>+ [TTA] | 1280<br>1536 | 55.0<br>**55.8** | 72.7<br>**72.7** | 3136<br>- | 26.2<br>- | 19.4<br>- | 140.7<br>- | 209.8<br>- |
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<details>
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<summary>Table Notes</summary>
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@ -228,7 +228,7 @@ YOLOv5 超级容易上手，简单易学。我们优先考虑现实世界的结
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| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
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| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
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| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
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| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)<br>+[TTA] | 1280<br>1536 | 55.0<br>**55.8** | 72.7<br>**72.7** | 3136<br>- | 26.2<br>- | 19.4<br>- | 140.7<br>- | 209.8<br>- |
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| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)<br>+[TTA] | 1280<br>1536 | 55.0<br>**55.8** | 72.7<br>**72.7** | 3136<br>- | 26.2<br>- | 19.4<br>- | 140.7<br>- | 209.8<br>- |
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<details>
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<summary>笔记</summary>
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@ -358,8 +358,8 @@ YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) 带来对
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- **准确性**都在单模型单尺度上计算，数据集使用 [ImageNet-1k](https://www.image-net.org/index.php) 。<br>复现命令 `python classify/val.py --data ../datasets/imagenet --img 224`
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- **推理速度**是使用 100 个推理图像进行平均得到，测试环境使用谷歌 [Colab Pro](https://colab.research.google.com/signup) V100 高 RAM 实例。<br>复现命令 `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`
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- **模型导出**到 FP32 的 ONNX 和 FP16 的 TensorRT 使用 `export.py` 。<br>复现命令 `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`
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</details>
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</details>
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</details>
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</details>
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<details>
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<summary>分类训练示例 <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/classify/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></summary>
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@ -28,42 +28,42 @@ The model inference results are returned as a JSON response:
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```json
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[
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{
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"class": 0,
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"confidence": 0.8900438547,
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"height": 0.9318675399,
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"name": "person",
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"width": 0.3264600933,
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"xcenter": 0.7438579798,
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"ycenter": 0.5207948685
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},
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{
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"class": 0,
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"confidence": 0.8440024257,
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"height": 0.7155083418,
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"name": "person",
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"width": 0.6546785235,
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"xcenter": 0.427829951,
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"ycenter": 0.6334488392
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},
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{
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"class": 27,
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"confidence": 0.3771208823,
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"height": 0.3902671337,
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"name": "tie",
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"width": 0.0696444362,
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"xcenter": 0.3675483763,
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"ycenter": 0.7991207838
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},
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{
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"class": 27,
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"confidence": 0.3527112305,
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"height": 0.1540903747,
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"name": "tie",
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"width": 0.0336618312,
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"xcenter": 0.7814827561,
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"ycenter": 0.5065554976
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}
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{
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"class": 0,
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"confidence": 0.8900438547,
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"height": 0.9318675399,
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"name": "person",
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"width": 0.3264600933,
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"xcenter": 0.7438579798,
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"ycenter": 0.5207948685
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},
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{
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"class": 0,
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"confidence": 0.8440024257,
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"height": 0.7155083418,
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"name": "person",
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"width": 0.6546785235,
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"xcenter": 0.427829951,
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"ycenter": 0.6334488392
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},
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{
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"class": 27,
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"confidence": 0.3771208823,
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"height": 0.3902671337,
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"name": "tie",
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"width": 0.0696444362,
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"xcenter": 0.3675483763,
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"ycenter": 0.7991207838
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},
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{
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"class": 27,
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"confidence": 0.3527112305,
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"height": 0.1540903747,
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"name": "tie",
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"width": 0.0336618312,
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"xcenter": 0.7814827561,
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"ycenter": 0.5065554976
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}
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]
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```
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