Negative weights when using Macbook M1 with MPS #1453
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| # Ultralytics 🚀 - AGPL-3.0 License https://ultralytics.com/license | |
| # Ultralytics Actions https://github.com/ultralytics/actions | |
| # This workflow automatically formats code and documentation in PRs to official Ultralytics standards | |
| name: Ultralytics Actions | |
| on: | |
| issues: | |
| types: [opened] | |
| pull_request_target: | |
| branches: [main, master] | |
| types: [opened, closed, synchronize, review_requested] | |
| jobs: | |
| format: | |
| runs-on: ubuntu-latest | |
| steps: | |
| - name: Run Ultralytics Formatting | |
| uses: ultralytics/actions@main | |
| with: | |
| token: ${{ secrets._GITHUB_TOKEN }} # note GITHUB_TOKEN automatically generated | |
| labels: true # autolabel issues and PRs | |
| python: true # format Python code and docstrings | |
| prettier: true # format YAML, JSON, Markdown and CSS | |
| spelling: true # check spelling | |
| links: false # check broken links | |
| summary: true # print PR summary with GPT4o (requires 'openai_api_key') | |
| openai_api_key: ${{ secrets.OPENAI_API_KEY }} | |
| first_issue_response: | | |
| 👋 Hello @${{ 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://docs.ultralytics.com/guides/model-training-tips//). | |
| ## Requirements | |
| [**Python>=3.8.0**](https://www.python.org/) with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). To get started: | |
| ```bash | |
| git clone https://github.com/ultralytics/yolov5 # clone | |
| cd yolov5 | |
| pip install -r requirements.txt # install | |
| ``` | |
| ## Environments | |
| 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: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/models/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> | |
| - **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/) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a> | |
| ## Status | |
| <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a> | |
| 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 🚀 | |
| We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - [YOLOv8](https://github.com/ultralytics/ultralytics) 🚀! | |
| Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects. | |
| Check out our [YOLOv8 Docs](https://docs.ultralytics.com/) for details and get started with: | |
| ```bash | |
| pip install ultralytics | |
| ``` |