diff --git a/.github/README_cn.md b/.github/README_cn.md index 4184c4c..90d3da8 100644 --- a/.github/README_cn.md +++ b/.github/README_cn.md @@ -144,47 +144,40 @@ python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml - -##
环境
-使用经过我们验证的环境，几秒钟就可以开始。点击下面的每个图标了解详情。 +##
Integrations
+ +
+ + +
+
- - - - - - - - - - - - - - - -
- -##
如何与第三方集成
- -
- - - - - - - - - + + + + + + + + +
-|Deci ⭐ NEW|ClearML ⭐ NEW|Roboflow|Weights & Biases +|Roboflow|ClearML ⭐ NEW|Comet ⭐ NEW|Deci ⭐ NEW| |:-:|:-:|:-:|:-:| -|在[Deci](https://bit.ly/yolov5-deci-platform)一键自动编译和量化YOLOv5以提高推理性能|使用[ClearML](https://cutt.ly/yolov5-readme-clearml) (开源!)自动追踪，可视化，以及远程训练YOLOv5|标记并将您的自定义数据直接导出到YOLOv5后，用[Roboflow](https://roboflow.com/?ref=ultralytics)进行训练 |通过[Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme)自动跟踪以及可视化你在云端所有的YOLOv5训练运行情况 +|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-comet) lets you save YOLOv5 models, resume training, and interactively visualise and debug predictions|Automatically compile and quantize YOLOv5 for better inference performance in one click at [Deci](https://bit.ly/yolov5-deci-platform)| + + +##
Ultralytics HUB
+ +[Ultralytics HUB](https://bit.ly/ultralytics_hub) is our ⭐ **NEW** no-code solution to visualize datasets, train YOLOv5 🚀 models, and deploy to the real world in a seamless experience. Get started for **Free** now! + + + ##
为什么选择 YOLOv5
diff --git a/README.md b/README.md index efe7d4b..5101297 100644 --- a/README.md +++ b/README.md @@ -52,9 +52,7 @@ ##
Documentation
-See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment. - -##
Quick Start Examples
+See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment. See below for quickstart examples.
Install @@ -71,7 +69,7 @@ pip install -r requirements.txt # install
-
+
Inference YOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest @@ -163,7 +161,11 @@ python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml - ##
Integrations
- +
+ + +
+
@@ -184,6 +186,14 @@ python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml - |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-comet) lets you save YOLOv5 models, resume training, and interactively visualise and debug predictions|Automatically compile and quantize YOLOv5 for better inference performance in one click at [Deci](https://bit.ly/yolov5-deci-platform)| +##
Ultralytics HUB
+ +[Ultralytics HUB](https://bit.ly/ultralytics_hub) is our ⭐ **NEW** no-code solution to visualize datasets, train YOLOv5 🚀 models, and deploy to the real world in a seamless experience. Get started for **Free** now! + + + + + ##
Why YOLOv5
YOLOv5 has been designed to be super easy to get started and simple to learn. We prioritize real-world results. diff --git a/segment/tutorial.ipynb b/segment/tutorial.ipynb index 4755997..079bfe3 100644 --- a/segment/tutorial.ipynb +++ b/segment/tutorial.ipynb @@ -232,7 +232,7 @@ "source": [ "# 3. Train\n", "\n", - "

\n", + "

\n", "Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n", "

\n", "\n", diff --git a/tutorial.ipynb b/tutorial.ipynb index b40f08e..96f0542 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -626,7 +626,7 @@ "source": [ "# 3. Train\n", "\n", - "

\n", + "

\n", "Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n", "

\n", "\n",