diff --git a/.github/workflows/ci-testing.yml b/.github/workflows/ci-testing.yml index bff95f654552..1ad6087921d6 100644 --- a/.github/workflows/ci-testing.yml +++ b/.github/workflows/ci-testing.yml @@ -45,6 +45,14 @@ jobs: python detect.py --weights ${{ matrix.model }}.onnx --img 320 python segment/predict.py --weights ${{ matrix.model }}-seg.onnx --img 320 python classify/predict.py --weights ${{ matrix.model }}-cls.onnx --img 224 + - name: Notify on failure + if: failure() && github.repository == 'ultralytics/yolov5' && (github.event_name == 'schedule' || github.event_name == 'push') + uses: slackapi/slack-github-action@v1.23.0 + with: + payload: | + {"text": " GitHub Actions error for ${{ github.workflow }} ❌\n\n\n*Repository:* https://github.com/${{ github.repository }}\n*Action:* https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}\n*Author:* ${{ github.actor }}\n*Event:* ${{ github.event_name }}\n*Job Status:* ${{ job.status }}\n"} + env: + SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_YOLO }} Tests: timeout-minutes: 60 @@ -151,3 +159,11 @@ jobs: for path in '$m', '$b': model = torch.hub.load('.', 'custom', path=path, source='local') EOF + - name: Notify on failure + if: failure() && github.repository == 'ultralytics/yolov5' && (github.event_name == 'schedule' || github.event_name == 'push') + uses: slackapi/slack-github-action@v1.23.0 + with: + payload: | + {"text": " GitHub Actions error for ${{ github.workflow }} ❌\n\n\n*Repository:* https://github.com/${{ github.repository }}\n*Action:* https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}\n*Author:* ${{ github.actor }}\n*Event:* ${{ github.event_name }}\n*Job Status:* ${{ job.status }}\n"} + env: + SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_YOLO }} diff --git a/.github/workflows/greetings.yml b/.github/workflows/greetings.yml index 337a563803db..8aca12d3c370 100644 --- a/.github/workflows/greetings.yml +++ b/.github/workflows/greetings.yml @@ -23,11 +23,11 @@ jobs: - ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_ — Bruce Lee issue-message: | - 👋 Hello @${{ github.actor }}, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ [Tutorials](https://docs.ultralytics.com/yolov5/#tutorials) to get started, where you can find quickstart guides for simple tasks like [Custom Data Training](https://docs.ultralytics.com/yolov5/train_custom_data/) all the way to advanced concepts like [Hyperparameter Evolution](https://docs.ultralytics.com/yolov5/hyp_evolution/). + 👋 Hello @${{ github.actor }}, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ [Tutorials](https://docs.ultralytics.com/yolov5/) to get started, where you can find quickstart guides for simple tasks like [Custom Data Training](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/) all the way to advanced concepts like [Hyperparameter Evolution](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/). If this is a 🐛 Bug Report, please provide a **minimum reproducible example** to help us debug it. - If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results). + If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our [Tips for Best Training Results](https://docs.ultralytics.com/yolov5/tutorials/tips_for_best_training_results/). ## Requirements @@ -43,15 +43,15 @@ jobs: 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): - **Notebooks** with free GPU: Run on Gradient Open In Colab Open In Kaggle - - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) - - **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart) - - **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) Docker Pulls + - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/) + - **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/) + - **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) Docker Pulls ## Status YOLOv5 CI - If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit. + If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit. ## Introducing YOLOv8 🚀 diff --git a/.github/workflows/links.yml b/.github/workflows/links.yml new file mode 100644 index 000000000000..a5413318030f --- /dev/null +++ b/.github/workflows/links.yml @@ -0,0 +1,38 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license +# YOLO Continuous Integration (CI) GitHub Actions tests + +name: Check Broken links + +on: + push: + branches: [master] + pull_request: + branches: [master] + workflow_dispatch: + schedule: + - cron: '0 0 * * *' # runs at 00:00 UTC every day + +jobs: + Links: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + + - name: Test Markdown and HTML links + uses: lycheeverse/lychee-action@v1.7.0 + with: + fail: true + # accept 429(Instagram, 'too many requests'), 999(LinkedIn, 'unknown status code'), Timeout(Twitter) + args: --accept 429,999 --exclude-loopback --exclude twitter.com --exclude-path '**/ci-testing.yaml' --exclude-mail './**/*.md' './**/*.html' + env: + GITHUB_TOKEN: ${{secrets.GITHUB_TOKEN}} + + - name: Test Markdown, HTML, YAML, Python and Notebook links + if: github.event_name == 'workflow_dispatch' + uses: lycheeverse/lychee-action@v1.7.0 + with: + fail: true + # accept 429(Instagram, 'too many requests'), 999(LinkedIn, 'unknown status code'), Timeout(Twitter) + args: --accept 429,999 --exclude-loopback --exclude twitter.com,url.com --exclude-path '**/ci-testing.yaml' --exclude-mail './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb' + env: + GITHUB_TOKEN: ${{secrets.GITHUB_TOKEN}} diff --git a/.github/workflows/stale.yml b/.github/workflows/stale.yml index 734350441c61..65c8f70798f1 100644 --- a/.github/workflows/stale.yml +++ b/.github/workflows/stale.yml @@ -12,26 +12,33 @@ jobs: - uses: actions/stale@v8 with: repo-token: ${{ secrets.GITHUB_TOKEN }} + stale-issue-message: | - 👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs. + 👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help. - Access additional [YOLOv5](https://ultralytics.com/yolov5) 🚀 resources: - - **Wiki** – https://github.com/ultralytics/yolov5/wiki - - **Tutorials** – https://github.com/ultralytics/yolov5#tutorials - - **Docs** – https://docs.ultralytics.com + For additional resources and information, please see the links below: - Access additional [Ultralytics](https://ultralytics.com) ⚡ resources: - - **Ultralytics HUB** – https://ultralytics.com/hub - - **Vision API** – https://ultralytics.com/yolov5 - - **About Us** – https://ultralytics.com/about - - **Join Our Team** – https://ultralytics.com/work - - **Contact Us** – https://ultralytics.com/contact + - **Docs**: https://docs.ultralytics.com + - **HUB**: https://hub.ultralytics.com + - **Community**: https://community.ultralytics.com Feel free to inform us of any other **issues** you discover or **feature requests** that come to mind in the future. Pull Requests (PRs) are also always welcomed! - Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐! + Thank you for your contributions to YOLO 🚀 and Vision AI ⭐ + + stale-pr-message: | + 👋 Hello there! We wanted to let you know that we've decided to close this pull request due to inactivity. We appreciate the effort you put into contributing to our project, but unfortunately, not all contributions are suitable or aligned with our product roadmap. + + We hope you understand our decision, and please don't let it discourage you from contributing to open source projects in the future. We value all of our community members and their contributions, and we encourage you to keep exploring new projects and ways to get involved. + + For additional resources and information, please see the links below: + + - **Docs**: https://docs.ultralytics.com + - **HUB**: https://hub.ultralytics.com + - **Community**: https://community.ultralytics.com + + Thank you for your contributions to YOLO 🚀 and Vision AI ⭐ - stale-pr-message: 'This pull request has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions YOLOv5 🚀 and Vision AI ⭐.' days-before-issue-stale: 30 days-before-issue-close: 10 days-before-pr-stale: 90 diff --git a/README.md b/README.md index e4258aa32592..5326816ce52c 100644 --- a/README.md +++ b/README.md @@ -85,7 +85,7 @@ 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 +YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). ```python @@ -134,7 +134,7 @@ The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5 results. [Models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are -1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://github.com/ultralytics/yolov5/issues/475) times faster). Use the +1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) times faster). Use the largest `--batch-size` possible, or pass `--batch-size -1` for YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB. @@ -153,22 +153,22 @@ python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml -
Tutorials -- [Train Custom Data](https://docs.ultralytics.com/yolov5/train_custom_data) 🚀 RECOMMENDED -- [Tips for Best Training Results](https://docs.ultralytics.com/yolov5/tips_for_best_training_results) ☘️ RECOMMENDED -- [Multi-GPU Training](https://docs.ultralytics.com/yolov5/multi_gpu_training) -- [PyTorch Hub](https://docs.ultralytics.com/yolov5/pytorch_hub) 🌟 NEW -- [TFLite, ONNX, CoreML, TensorRT Export](https://docs.ultralytics.com/yolov5/export) 🚀 -- [NVIDIA Jetson platform Deployment](https://docs.ultralytics.com/yolov5/jetson_nano) 🌟 NEW -- [Test-Time Augmentation (TTA)](https://docs.ultralytics.com/yolov5/tta) -- [Model Ensembling](https://docs.ultralytics.com/yolov5/ensemble) -- [Model Pruning/Sparsity](https://docs.ultralytics.com/yolov5/pruning_sparsity) -- [Hyperparameter Evolution](https://docs.ultralytics.com/yolov5/hyp_evolution) -- [Transfer Learning with Frozen Layers](https://docs.ultralytics.com/yolov5/transfer_learn_frozen) -- [Architecture Summary](https://docs.ultralytics.com/yolov5/architecture) 🌟 NEW -- [Roboflow for Datasets](https://docs.ultralytics.com/yolov5/roboflow) -- [ClearML Logging](https://docs.ultralytics.com/yolov5/clearml) 🌟 NEW -- [YOLOv5 with Neural Magic's Deepsparse](https://docs.ultralytics.com/yolov5/neural_magic) 🌟 NEW -- [Comet Logging](https://docs.ultralytics.com/yolov5/comet) 🌟 NEW +- [Train Custom Data](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data) 🚀 RECOMMENDED +- [Tips for Best Training Results](https://docs.ultralytics.com/yolov5/tutorials/tips_for_best_training_results) ☘️ +- [Multi-GPU Training](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) +- [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) 🌟 NEW +- [TFLite, ONNX, CoreML, TensorRT Export](https://docs.ultralytics.com/yolov5/tutorials/model_export) 🚀 +- [NVIDIA Jetson platform Deployment](https://docs.ultralytics.com/yolov5/tutorials/running_on_jetson_nano) 🌟 NEW +- [Test-Time Augmentation (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation) +- [Model Ensembling](https://docs.ultralytics.com/yolov5/tutorials/model_ensembling) +- [Model Pruning/Sparsity](https://docs.ultralytics.com/yolov5/tutorials/model_pruning_and_sparsity) +- [Hyperparameter Evolution](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution) +- [Transfer Learning with Frozen Layers](https://docs.ultralytics.com/yolov5/tutorials/transfer_learning_with_frozen_layers) +- [Architecture Summary](https://docs.ultralytics.com/yolov5/tutorials/architecture_description) 🌟 NEW +- [Roboflow for Datasets, Labeling, and Active Learning](https://docs.ultralytics.com/yolov5/tutorials/roboflow_datasets_integration) +- [ClearML Logging](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) 🌟 NEW +- [YOLOv5 with Neural Magic's Deepsparse](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization) 🌟 NEW +- [Comet Logging](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration) 🌟 NEW
@@ -247,7 +247,7 @@ YOLOv5 has been designed to be super easy to get started and simple to learn. We - All checkpoints are trained to 300 epochs with default settings. Nano and Small models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, all others use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml). - **mAPval** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` - **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.
Reproduce by `python val.py --data coco.yaml --img 640 --task speed --batch 1` -- **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale augmentations.
Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment` +- **TTA** [Test Time Augmentation](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation) includes reflection and scale augmentations.
Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
@@ -309,7 +309,7 @@ python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # val Use pretrained YOLOv5m-seg.pt to predict bus.jpg: ```bash -python segment/predict.py --weights yolov5m-seg.pt --data data/images/bus.jpg +python segment/predict.py --weights yolov5m-seg.pt --source data/images/bus.jpg ``` ```python @@ -400,7 +400,7 @@ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --im Use pretrained YOLOv5s-cls.pt to predict bus.jpg: ```bash -python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg +python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg ``` ```python @@ -436,10 +436,10 @@ Get started in seconds with our verified environments. Click each icon below for - + - + @@ -484,4 +484,4 @@ For YOLOv5 bug reports and feature requests please visit [GitHub Issues](https:/ -[tta]: https://github.com/ultralytics/yolov5/issues/303 +[tta]: https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation diff --git a/README.zh-CN.md b/README.zh-CN.md index 0a696e591d0d..913f817a3c14 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -80,7 +80,7 @@ pip install -r requirements.txt # install
推理 -使用 YOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) 推理。最新 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 将自动的从 +使用 YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) 推理。最新 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 将自动的从 YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载。 ```python @@ -128,7 +128,7 @@ python detect.py --weights yolov5s.pt --source 0 # 下面的命令重现 YOLOv5 在 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) 数据集上的结果。 最新的 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 和 [数据集](https://github.com/ultralytics/yolov5/tree/master/data) 将自动的从 YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载。 -YOLOv5n/s/m/l/x 在 V100 GPU 的训练时间为 1/2/4/6/8 天（ [多GPU](https://github.com/ultralytics/yolov5/issues/475) 训练速度更快）。 +YOLOv5n/s/m/l/x 在 V100 GPU 的训练时间为 1/2/4/6/8 天（ [多GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) 训练速度更快）。 尽可能使用更大的 `--batch-size` ，或通过 `--batch-size -1` 实现 YOLOv5 [自动批处理](https://github.com/ultralytics/yolov5/pull/5092) 。下方显示的 batchsize 适用于 V100-16GB。 @@ -147,22 +147,22 @@ python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml -
教程 -- [训练自定义数据](https://docs.ultralytics.com/yolov5/train_custom_data) 🚀 推荐 -- [获得最佳训练结果的技巧](https://docs.ultralytics.com/yolov5/tips_for_best_training_results) ☘️ 推荐 -- [多 GPU 训练](https://docs.ultralytics.com/yolov5/multi_gpu_training) -- [PyTorch Hub](https://docs.ultralytics.com/yolov5/pytorch_hub) 🌟 新 -- [TFLite, ONNX, CoreML, TensorRT 导出](https://docs.ultralytics.com/yolov5/export) 🚀 -- [NVIDIA Jetson 平台部署](https://docs.ultralytics.com/yolov5/jetson_nano) 🌟 新 -- [测试时增强（TTA）](https://docs.ultralytics.com/yolov5/tta) -- [模型集成](https://docs.ultralytics.com/yolov5/ensemble) -- [模型剪枝/稀疏性](https://docs.ultralytics.com/yolov5/pruning_sparsity) -- [超参数进化](https://docs.ultralytics.com/yolov5/hyp_evolution) -- [冻结层的迁移学习](https://docs.ultralytics.com/yolov5/transfer_learn_frozen) -- [架构概述](https://docs.ultralytics.com/yolov5/architecture) 🌟 新 -- [Roboflow](https://docs.ultralytics.com/yolov5/roboflow) -- [ClearML 日志记录](https://docs.ultralytics.com/yolov5/clearml) 🌟 新 -- [YOLOv5 与 Neural Magic 的 Deepsparse](https://docs.ultralytics.com/yolov5/neural_magic) 🌟 新 -- [Comet 日志记录](https://docs.ultralytics.com/yolov5/comet) 🌟 新 +- [训练自定义数据](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data) 🚀 推荐 +- [获得最佳训练结果的技巧](https://docs.ultralytics.com/yolov5/tutorials/tips_for_best_training_results) ☘️ +- [多GPU训练](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) +- [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) 🌟 新 +- [TFLite，ONNX，CoreML，TensorRT导出](https://docs.ultralytics.com/yolov5/tutorials/model_export) 🚀 +- [NVIDIA Jetson平台部署](https://docs.ultralytics.com/yolov5/tutorials/running_on_jetson_nano) 🌟 新 +- [测试时增强 (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation) +- [模型集成](https://docs.ultralytics.com/yolov5/tutorials/model_ensembling) +- [模型剪枝/稀疏](https://docs.ultralytics.com/yolov5/tutorials/model_pruning_and_sparsity) +- [超参数进化](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution) +- [冻结层的迁移学习](https://docs.ultralytics.com/yolov5/tutorials/transfer_learning_with_frozen_layers) +- [架构概述](https://docs.ultralytics.com/yolov5/tutorials/architecture_description) 🌟 新 +- [Roboflow用于数据集、标注和主动学习](https://docs.ultralytics.com/yolov5/tutorials/roboflow_datasets_integration) +- [ClearML日志记录](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) 🌟 新 +- [使用Neural Magic的Deepsparse的YOLOv5](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization) 🌟 新 +- [Comet日志记录](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration) 🌟 新
@@ -241,7 +241,7 @@ YOLOv5 超级容易上手，简单易学。我们优先考虑现实世界的结 - 所有模型都使用默认配置，训练 300 epochs。n和s模型使用 [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) ，其他模型都使用 [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml) 。 - \*\*mAPval\*\*在单模型单尺度上计算，数据集使用 [COCO val2017](http://cocodataset.org) 。
复现命令 `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` - **推理速度**在 COCO val 图像总体时间上进行平均得到，测试环境使用[AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/)实例。 NMS 时间 (大约 1 ms/img) 不包括在内。
复现命令 `python val.py --data coco.yaml --img 640 --task speed --batch 1` -- **TTA** [测试时数据增强](https://github.com/ultralytics/yolov5/issues/303) 包括反射和尺度变换。
复现命令 `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment` +- **TTA** [测试时数据增强](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation) 包括反射和尺度变换。
复现命令 `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
@@ -305,7 +305,7 @@ python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # 验 使用预训练的 YOLOv5m-seg.pt 来预测 bus.jpg： ```bash -python segment/predict.py --weights yolov5m-seg.pt --data data/images/bus.jpg +python segment/predict.py --weights yolov5m-seg.pt --source data/images/bus.jpg ``` ```python @@ -395,7 +395,7 @@ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --im 使用预训练的 YOLOv5s-cls.pt 来预测 bus.jpg： ```bash -python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg +python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg ``` ```python @@ -431,10 +431,10 @@ python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --inclu - + - + @@ -456,7 +456,7 @@ YOLOv5 在两种不同的 License 下可用： ##
联系我们
-请访问 [GitHub Issues](https://github.com/ultralytics/yolov5/issues) 或 [Ultralytics Community Forum](https://community.ultralytis.com) 以报告 YOLOv5 错误和请求功能。 +请访问 [GitHub Issues](https://github.com/ultralytics/yolov5/issues) 或 [Ultralytics Community Forum](https://community.ultralytics.com) 以报告 YOLOv5 错误和请求功能。
@@ -479,4 +479,4 @@ YOLOv5 在两种不同的 License 下可用：
-[tta]: https://github.com/ultralytics/yolov5/issues/303 +[tta]: https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation diff --git a/classify/tutorial.ipynb b/classify/tutorial.ipynb index 58723608bdbe..ddf67c5519f5 100644 --- a/classify/tutorial.ipynb +++ b/classify/tutorial.ipynb @@ -1350,7 +1350,7 @@ "export COMET_API_KEY= # 2. paste API key\n", "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\n", "```\n", - "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/comet). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n", + "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n", "\n", "\n", @@ -1372,7 +1372,7 @@ "\n", "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n", "\n", - "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) for details!\n", + "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!\n", "\n", "\n", "\"ClearML" @@ -1404,9 +1404,9 @@ "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", - "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n", - "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n", - "- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) \"Docker\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/hyps/hyp.no-augmentation.yaml b/data/hyps/hyp.no-augmentation.yaml index 0ae796c16dc2..8da18150538b 100644 --- a/data/hyps/hyp.no-augmentation.yaml +++ b/data/hyps/hyp.no-augmentation.yaml @@ -21,7 +21,7 @@ anchor_t: 4.0 # anchor-multiple threshold # this parameters are all zero since we want to use albumentation framework fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) hsv_h: 0 # image HSV-Hue augmentation (fraction) -hsv_s: 00 # image HSV-Saturation augmentation (fraction) +hsv_s: 0 # image HSV-Saturation augmentation (fraction) hsv_v: 0 # image HSV-Value augmentation (fraction) degrees: 0.0 # image rotation (+/- deg) translate: 0 # image translation (+/- fraction) diff --git a/hubconf.py b/hubconf.py index 9d820a54f290..73caf06685da 100644 --- a/hubconf.py +++ b/hubconf.py @@ -73,7 +73,7 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo return model.to(device) except Exception as e: - help_url = 'https://github.com/ultralytics/yolov5/issues/36' + help_url = 'https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading' s = f'{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help.' raise Exception(s) from e diff --git a/segment/train.py b/segment/train.py index 7e600f77d571..073fc742005b 100644 --- a/segment/train.py +++ b/segment/train.py @@ -12,7 +12,7 @@ Models: https://github.com/ultralytics/yolov5/tree/master/models Datasets: https://github.com/ultralytics/yolov5/tree/master/data -Tutorial: https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data +Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data """ import argparse @@ -167,8 +167,10 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio # DP mode if cuda and RANK == -1 and torch.cuda.device_count() > 1: - LOGGER.warning('WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n' - 'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.') + LOGGER.warning( + 'WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n' + 'See Multi-GPU Tutorial at https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training to get started.' + ) model = torch.nn.DataParallel(model) # SyncBatchNorm diff --git a/segment/tutorial.ipynb b/segment/tutorial.ipynb index cb52045bcb25..6e5caf53b8ff 100644 --- a/segment/tutorial.ipynb +++ b/segment/tutorial.ipynb @@ -463,7 +463,7 @@ "export COMET_API_KEY= # 2. paste API key\n", "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\n", "```\n", - "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/comet). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n", + "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n", "\n", "\n", @@ -485,7 +485,7 @@ "\n", "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n", "\n", - "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) for details!\n", + "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!\n", "\n", "\n", "\"ClearML" @@ -517,9 +517,9 @@ "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", - "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n", - "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n", - "- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) \"Docker\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/train.py b/train.py index 9a92af74dce2..a5a5175e53a3 100644 --- a/train.py +++ b/train.py @@ -12,7 +12,7 @@ Models: https://github.com/ultralytics/yolov5/tree/master/models Datasets: https://github.com/ultralytics/yolov5/tree/master/data -Tutorial: https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data +Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data """ import argparse @@ -175,8 +175,10 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio # DP mode if cuda and RANK == -1 and torch.cuda.device_count() > 1: - LOGGER.warning('WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n' - 'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.') + LOGGER.warning( + 'WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n' + 'See Multi-GPU Tutorial at https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training to get started.' + ) model = torch.nn.DataParallel(model) # SyncBatchNorm diff --git a/tutorial.ipynb b/tutorial.ipynb index d2b54c9c60ef..be87068822af 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -498,7 +498,7 @@ "export COMET_API_KEY= # 2. paste API key\n", "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\n", "```\n", - "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/comet). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n", + "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n", "\n", "\n", @@ -520,7 +520,7 @@ "\n", "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n", "\n", - "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) for details!\n", + "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!\n", "\n", "\n", "\"ClearML" @@ -555,9 +555,9 @@ "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", - "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n", - "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n", - "- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) \"Docker\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/utils/dataloaders.py b/utils/dataloaders.py index 55360ae4f7d7..327dea95891d 100644 --- a/utils/dataloaders.py +++ b/utils/dataloaders.py @@ -36,7 +36,7 @@ from utils.torch_utils import torch_distributed_zero_first # Parameters -HELP_URL = 'See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data' +HELP_URL = 'See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data' IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm' # include image suffixes VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv' # include video suffixes LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html diff --git a/utils/docker/Dockerfile-arm64 b/utils/docker/Dockerfile-arm64 index 556ae59a8700..7b5c610e5071 100644 --- a/utils/docker/Dockerfile-arm64 +++ b/utils/docker/Dockerfile-arm64 @@ -3,7 +3,7 @@ # Image is aarch64-compatible for Apple M1 and other ARM architectures i.e. Jetson Nano and Raspberry Pi # Start FROM Ubuntu image https://hub.docker.com/_/ubuntu -FROM arm64v8/ubuntu:rolling +FROM arm64v8/ubuntu:22.10 # Downloads to user config dir ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ diff --git a/utils/docker/Dockerfile-cpu b/utils/docker/Dockerfile-cpu index 8e8c23bf952f..613bdffa4768 100644 --- a/utils/docker/Dockerfile-cpu +++ b/utils/docker/Dockerfile-cpu @@ -3,7 +3,7 @@ # Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv5 deployments # Start FROM Ubuntu image https://hub.docker.com/_/ubuntu -FROM ubuntu:rolling +FROM ubuntu:22.10 # Downloads to user config dir ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ diff --git a/utils/loggers/__init__.py b/utils/loggers/__init__.py index 9b4c1d13b778..c7c283b728ac 100644 --- a/utils/loggers/__init__.py +++ b/utils/loggers/__init__.py @@ -118,7 +118,7 @@ def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, self.clearml = None prefix = colorstr('ClearML: ') LOGGER.warning(f'{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging.' - f' See https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml#readme') + f' See https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration#readme') else: self.clearml = None diff --git a/utils/loggers/comet/README.md b/utils/loggers/comet/README.md index 47e6a45654b8..aee8d16a336c 100644 --- a/utils/loggers/comet/README.md +++ b/utils/loggers/comet/README.md @@ -164,7 +164,7 @@ env COMET_LOG_PER_CLASS_METRICS=true python train.py \ If you would like to store your data using [Comet Artifacts](https://www.comet.com/docs/v2/guides/data-management/using-artifacts/#learn-more?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github), you can do so using the `upload_dataset` flag. -The dataset be organized in the way described in the [YOLOv5 documentation](https://docs.ultralytics.com/tutorials/train-custom-datasets/#3-organize-directories). The dataset config `yaml` file must follow the same format as that of the `coco128.yaml` file. +The dataset be organized in the way described in the [YOLOv5 documentation](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/). The dataset config `yaml` file must follow the same format as that of the `coco128.yaml` file. ```shell python train.py \