From 94e72a299c578962261faac4e50b4f64e11bef7e Mon Sep 17 00:00:00 2001 From: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com> Date: Sat, 19 Oct 2024 17:45:48 +0200 Subject: [PATCH] Ultralytics Refactor https://ultralytics.com/actions (#2295) * Refactor code for speed and clarity * Auto-format by https://ultralytics.com/actions --- README.md | 10 +++++----- README.zh-CN.md | 10 +++++----- classify/tutorial.ipynb | 4 ++-- data/coco128-seg.yaml | 2 +- data/coco128.yaml | 2 +- segment/tutorial.ipynb | 6 +++--- tutorial.ipynb | 6 +++--- 7 files changed, 20 insertions(+), 20 deletions(-) diff --git a/README.md b/README.md index fc5b50ffb1..68a54593b1 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@
Run on Gradient Open In Colab - Open In Kaggle + Open In Kaggle
@@ -182,9 +182,9 @@ Our key integrations with leading AI platforms extend the functionality of Ultra NeuralMagic logo -| Ultralytics HUB 🚀 | W&B | Comet ⭐ NEW | Neural Magic | -| :----------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | -| Streamline YOLO workflows: Label, train, and deploy effortlessly with [Ultralytics HUB](https://ultralytics.com/hub). Try now! | Track experiments, hyperparameters, and results with [Weights & Biases](https://docs.wandb.ai/guides/integrations/ultralytics/) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet) lets you save YOLO models, resume training, and interactively visualize and debug predictions | Run YOLO11 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) | +| Ultralytics HUB 🚀 | W&B | Comet ⭐ NEW | Neural Magic | +| :--------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | +| Streamline YOLO workflows: Label, train, and deploy effortlessly with [Ultralytics HUB](https://www.ultralytics.com/hub). Try now! | Track experiments, hyperparameters, and results with [Weights & Biases](https://docs.wandb.ai/guides/integrations/ultralytics/) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet) lets you save YOLO models, resume training, and interactively visualize and debug predictions | Run YOLO11 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) | ##
Ultralytics HUB
@@ -416,7 +416,7 @@ Get started in seconds with our verified environments. Click each icon below for - + diff --git a/README.zh-CN.md b/README.zh-CN.md index 91aa2f4bb8..d763a30a65 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -14,7 +14,7 @@
Run on Gradient Open In Colab - Open In Kaggle + Open In Kaggle
@@ -182,9 +182,9 @@ python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml - NeuralMagic logo -| Ultralytics HUB 🚀 | W&B | Comet ⭐ 全新 | Neural Magic | -| :------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------: | -| 简化 YOLO 工作流程：通过 [Ultralytics HUB](https://ultralytics.com/hub) 轻松标注、训练和部署。立即试用！ | 使用 [Weights & Biases](https://docs.wandb.ai/guides/integrations/ultralytics/) 跟踪实验、超参数和结果 | 永久免费，[Comet](https://bit.ly/yolov5-readme-comet) 允许您保存 YOLO11 模型、恢复训练，并交互式地可视化和调试预测结果 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) 运行 YOLO11 推理，速度提升至 6 倍 | +| Ultralytics HUB 🚀 | W&B | Comet ⭐ 全新 | Neural Magic | +| :----------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------: | +| 简化 YOLO 工作流程：通过 [Ultralytics HUB](https://www.ultralytics.com/hub) 轻松标注、训练和部署。立即试用！ | 使用 [Weights & Biases](https://docs.wandb.ai/guides/integrations/ultralytics/) 跟踪实验、超参数和结果 | 永久免费，[Comet](https://bit.ly/yolov5-readme-comet) 允许您保存 YOLO11 模型、恢复训练，并交互式地可视化和调试预测结果 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) 运行 YOLO11 推理，速度提升至 6 倍 | ##
Ultralytics HUB
@@ -417,7 +417,7 @@ python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --inclu - + diff --git a/classify/tutorial.ipynb b/classify/tutorial.ipynb index 3252daf718..8b819639a7 100644 --- a/classify/tutorial.ipynb +++ b/classify/tutorial.ipynb @@ -15,7 +15,7 @@ "
\n", " \"Run\n", " \"Open\n", - " \"Open\n", + " \"Open\n", "
\n", "\n", "This YOLOv5 🚀 notebook by Ultralytics presents simple train, validate and predict examples to help start your AI adventure.
See GitHub for community support or contact us for professional support.\n", @@ -1410,7 +1410,7 @@ "\n", "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n", "\n", - "- **Notebooks** with free GPU: \"Run \"Open \"Open\n", + "- **Notebooks** with free GPU: \"Run \"Open \"Open\n", "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n", "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n", "- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) \"Docker\n" diff --git a/data/coco128-seg.yaml b/data/coco128-seg.yaml index 363ced868a..6509214555 100644 --- a/data/coco128-seg.yaml +++ b/data/coco128-seg.yaml @@ -1,5 +1,5 @@ # Ultralytics YOLOv3 🚀, AGPL-3.0 license -# COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics +# COCO128-seg dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics # Example usage: python train.py --data coco128.yaml # parent # ├── yolov5 diff --git a/data/coco128.yaml b/data/coco128.yaml index 4bfbf3a2eb..78d1545c10 100644 --- a/data/coco128.yaml +++ b/data/coco128.yaml @@ -1,5 +1,5 @@ # Ultralytics YOLOv3 🚀, AGPL-3.0 license -# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics +# COCO128 dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics # Example usage: python train.py --data coco128.yaml # parent # ├── yolov5 diff --git a/segment/tutorial.ipynb b/segment/tutorial.ipynb index 55eaa254a1..1c364d6abe 100644 --- a/segment/tutorial.ipynb +++ b/segment/tutorial.ipynb @@ -15,7 +15,7 @@ "
\n", " \"Run\n", " \"Open\n", - " \"Open\n", + " \"Open\n", "
\n", "\n", "This YOLOv5 🚀 notebook by Ultralytics presents simple train, validate and predict examples to help start your AI adventure.
See GitHub for community support or contact us for professional support.\n", @@ -222,7 +222,7 @@ "Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n", "

\n", "\n", - "Train a YOLOv5s-seg model on the [COCO128](https://www.kaggle.com/ultralytics/coco128) dataset with `--data coco128-seg.yaml`, starting from pretrained `--weights yolov5s-seg.pt`, or from randomly initialized `--weights '' --cfg yolov5s-seg.yaml`.\n", + "Train a YOLOv5s-seg model on the [COCO128](https://www.kaggle.com/datasets/ultralytics/coco128) dataset with `--data coco128-seg.yaml`, starting from pretrained `--weights yolov5s-seg.pt`, or from randomly initialized `--weights '' --cfg yolov5s-seg.yaml`.\n", "\n", "- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n", "automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n", @@ -523,7 +523,7 @@ "\n", "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n", "\n", - "- **Notebooks** with free GPU: \"Run \"Open \"Open\n", + "- **Notebooks** with free GPU: \"Run \"Open \"Open\n", "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n", "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n", "- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) \"Docker\n" diff --git a/tutorial.ipynb b/tutorial.ipynb index 783c5ef718..9e688bfe76 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -28,7 +28,7 @@ "
\n", " \"Run\n", " \"Open\n", - " \"Open\n", + " \"Open\n", "
\n", "\n", "This YOLOv5 🚀 notebook by Ultralytics presents simple train, validate and predict examples to help start your AI adventure.
We hope that the resources in this notebook will help you get the most out of YOLOv5. Please browse the YOLOv5 Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions!\n", @@ -258,7 +258,7 @@ "Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n", "

\n", "\n", - "Train a YOLOv5s model on the [COCO128](https://www.kaggle.com/ultralytics/coco128) dataset with `--data coco128.yaml`, starting from pretrained `--weights yolov5s.pt`, or from randomly initialized `--weights '' --cfg yolov5s.yaml`.\n", + "Train a YOLOv5s model on the [COCO128](https://www.kaggle.com/datasets/ultralytics/coco128) dataset with `--data coco128.yaml`, starting from pretrained `--weights yolov5s.pt`, or from randomly initialized `--weights '' --cfg yolov5s.yaml`.\n", "\n", "- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n", "automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n", @@ -554,7 +554,7 @@ "\n", "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n", "\n", - "- **Notebooks** with free GPU: \"Run \"Open \"Open\n", + "- **Notebooks** with free GPU: \"Run \"Open \"Open\n", "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n", "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n", "- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) \"Docker\n"