{"id":13443239,"url":"https://github.com/ultralytics/yolov3","last_synced_at":"2025-05-12T13:13:12.348Z","repository":{"id":37733936,"uuid":"146165888","full_name":"ultralytics/yolov3","owner":"ultralytics","description":"YOLOv3 in PyTorch \u003e ONNX \u003e CoreML \u003e 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Data Science Toolbox","对象检测、分割","Other Versions of YOLO","Paper implementations｜论文实现","Paper implementations","Research \u0026 Data Analysis"],"sub_categories":["Uncategorized","Deep Learning Packages","网络服务_其他","Other libraries｜其他库:","Other libraries:"],"readme":"\u003cdiv align=\"center\"\u003e\n  \u003cp\u003e\n    \u003ca href=\"https://www.ultralytics.com/yolo\" target=\"_blank\"\u003e\n      \u003cimg width=\"100%\" src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov3/banner-yolov3.png\" alt=\"Ultralytics YOLOv3 banner\"\u003e\u003c/a\u003e\n  \u003c/p\u003e\n\n[中文](https://docs.ultralytics.com/zh) | [한국어](https://docs.ultralytics.com/ko) | [日本語](https://docs.ultralytics.com/ja) | [Русский](https://docs.ultralytics.com/ru) | [Deutsch](https://docs.ultralytics.com/de) | [Français](https://docs.ultralytics.com/fr) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt) | [Türkçe](https://docs.ultralytics.com/tr) | [Tiếng Việt](https://docs.ultralytics.com/vi) | [العربية](https://docs.ultralytics.com/ar)\n\n\u003cdiv\u003e\n    \u003ca href=\"https://github.com/ultralytics/yolov3/actions/workflows/ci-testing.yml\"\u003e\u003cimg src=\"https://github.com/ultralytics/yolov3/actions/workflows/ci-testing.yml/badge.svg\" alt=\"YOLOv3 CI\"\u003e\u003c/a\u003e\n    \u003ca href=\"https://zenodo.org/badge/latestdoi/264818686\"\u003e\u003cimg src=\"https://zenodo.org/badge/264818686.svg\" alt=\"YOLOv3 Citation\"\u003e\u003c/a\u003e\n    \u003ca href=\"https://hub.docker.com/r/ultralytics/yolov3\"\u003e\u003cimg src=\"https://img.shields.io/docker/pulls/ultralytics/yolov3?logo=docker\" alt=\"Docker Pulls\"\u003e\u003c/a\u003e\n    \u003ca href=\"https://discord.com/invite/ultralytics\"\u003e\u003cimg alt=\"Discord\" src=\"https://img.shields.io/discord/1089800235347353640?logo=discord\u0026logoColor=white\u0026label=Discord\u0026color=blue\"\u003e\u003c/a\u003e\n    \u003ca href=\"https://community.ultralytics.com/\"\u003e\u003cimg alt=\"Ultralytics Forums\" src=\"https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com\u0026logo=discourse\u0026label=Forums\u0026color=blue\"\u003e\u003c/a\u003e\n    \u003ca href=\"https://www.reddit.com/r/ultralytics/\"\u003e\u003cimg alt=\"Ultralytics Reddit\" src=\"https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat\u0026logo=reddit\u0026logoColor=white\u0026label=Reddit\u0026color=blue\"\u003e\u003c/a\u003e\n    \u003cbr\u003e\n    \u003ca href=\"https://bit.ly/yolov5-paperspace-notebook\"\u003e\u003cimg src=\"https://assets.paperspace.io/img/gradient-badge.svg\" alt=\"Run on Gradient\"\u003e\u003c/a\u003e\n    \u003ca href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"\u003e\u003c/a\u003e\n    \u003ca href=\"https://www.kaggle.com/models/ultralytics/yolov5\"\u003e\u003cimg src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"\u003e\u003c/a\u003e\n  \u003c/div\u003e\n  \u003cbr\u003e\n\nUltralytics YOLOv3 is a robust and efficient [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) model developed by [Ultralytics](https://www.ultralytics.com/). Built on the [PyTorch](https://pytorch.org/) framework, this implementation extends the original YOLOv3 architecture, renowned for its improvements in [object detection](https://www.ultralytics.com/glossary/object-detection) speed and accuracy over earlier versions. It incorporates best practices and insights from extensive research, making it a reliable choice for a wide range of vision AI applications.\n\nExplore the [Ultralytics Docs](https://docs.ultralytics.com/) for in-depth guidance (YOLOv3-specific docs may be limited, but general YOLO principles apply), open an issue on [GitHub](https://github.com/ultralytics/yolov5/issues/new/choose) for support, and join our [Discord community](https://discord.com/invite/ultralytics) for questions and discussions!\n\nFor Enterprise License requests, please complete the form at [Ultralytics Licensing](https://www.ultralytics.com/license).\n\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://github.com/ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png\" width=\"2%\" alt=\"Ultralytics GitHub\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\" alt=\"space\"\u003e\n  \u003ca href=\"https://www.linkedin.com/company/ultralytics/\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png\" width=\"2%\" alt=\"Ultralytics LinkedIn\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\" alt=\"space\"\u003e\n  \u003ca href=\"https://twitter.com/ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png\" width=\"2%\" alt=\"Ultralytics Twitter\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\" alt=\"space\"\u003e\n  \u003ca href=\"https://youtube.com/ultralytics?sub_confirmation=1\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png\" width=\"2%\" alt=\"Ultralytics YouTube\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\" alt=\"space\"\u003e\n  \u003ca href=\"https://www.tiktok.com/@ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png\" width=\"2%\" alt=\"Ultralytics TikTok\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\" alt=\"space\"\u003e\n  \u003ca href=\"https://ultralytics.com/bilibili\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png\" width=\"2%\" alt=\"Ultralytics BiliBili\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\" alt=\"space\"\u003e\n  \u003ca href=\"https://discord.com/invite/ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png\" width=\"2%\" alt=\"Ultralytics Discord\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cbr\u003e\n\n## 🚀 YOLO11: The Next Evolution\n\nWe are thrilled to introduce **Ultralytics YOLO11** 🚀, the latest advancement in our state-of-the-art vision models! Available now at the [Ultralytics YOLO GitHub repository](https://github.com/ultralytics/ultralytics), YOLO11 continues our legacy of speed, precision, and user-friendly design. Whether you're working on [object detection](https://docs.ultralytics.com/tasks/detect/), [instance segmentation](https://docs.ultralytics.com/tasks/segment/), [pose estimation](https://docs.ultralytics.com/tasks/pose/), [image classification](https://docs.ultralytics.com/tasks/classify/), or [oriented object detection (OBB)](https://docs.ultralytics.com/tasks/obb/), YOLO11 delivers the performance and flexibility needed for modern computer vision tasks.\n\nGet started today and unlock the full potential of YOLO11! Visit the [Ultralytics Docs](https://docs.ultralytics.com/) for comprehensive guides and resources:\n\n[![PyPI version](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/projects/ultralytics)\n\n```bash\n# Install the ultralytics package\npip install ultralytics\n```\n\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://www.ultralytics.com/yolo\" target=\"_blank\"\u003e\n  \u003cimg width=\"100%\" src=\"https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png\" alt=\"Ultralytics YOLO Performance Comparison\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n## 📚 Documentation\n\nSee the [Ultralytics Docs for YOLOv3](https://docs.ultralytics.com/models/yolov3/) for full documentation on training, testing, and deployment using the Ultralytics framework. While YOLOv3-specific documentation may be limited, the general YOLO principles apply. Below are quickstart examples adapted for YOLOv3 concepts.\n\n\u003cdetails open\u003e\n\u003csummary\u003eInstall\u003c/summary\u003e\n\nClone the repository and install dependencies from `requirements.txt` in a [**Python\u003e=3.8.0**](https://www.python.org/) environment. Ensure you have [**PyTorch\u003e=1.8**](https://pytorch.org/get-started/locally/) installed. (Note: This repo is originally YOLOv5, dependencies should be compatible but tailored testing for YOLOv3 is recommended).\n\n```bash\n# Clone the YOLOv3 repository\ngit clone https://github.com/ultralytics/yolov3\n\n# Navigate to the cloned directory\ncd yolov3\n\n# Install required packages\npip install -r requirements.txt\n```\n\n\u003c/details\u003e\n\n\u003cdetails open\u003e\n\u003csummary\u003eInference with PyTorch Hub\u003c/summary\u003e\n\nUse YOLOv3 via [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/) for inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) like `yolov3.pt`, `yolov3-spp.pt`, `yolov3-tiny.pt` can be loaded.\n\n```python\nimport torch\n\n# Load a YOLOv3 model (e.g., yolov3, yolov3-spp)\nmodel = torch.hub.load(\"ultralytics/yolov3\", \"yolov3\", pretrained=True)  # specify 'yolov3' or other variants\n\n# Define the input image source (URL, local file, PIL image, OpenCV frame, numpy array, or list)\nimg = \"https://ultralytics.com/images/zidane.jpg\"  # Example image\n\n# Perform inference\nresults = model(img)\n\n# Process the results (options: .print(), .show(), .save(), .crop(), .pandas())\nresults.print()  # Print results to console\nresults.show()  # Display results in a window\nresults.save()  # Save results to runs/detect/exp\n```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003eInference with detect.py\u003c/summary\u003e\n\nThe `detect.py` script runs inference on various sources. Use `--weights yolov3.pt` or other YOLOv3 variants. It automatically downloads models and saves results to `runs/detect`.\n\n```bash\n# Run inference using a webcam with yolov3-tiny\npython detect.py --weights yolov3-tiny.pt --source 0\n\n# Run inference on a local image file with yolov3\npython detect.py --weights yolov3.pt --source img.jpg\n\n# Run inference on a local video file with yolov3-spp\npython detect.py --weights yolov3-spp.pt --source vid.mp4\n\n# Run inference on a screen capture\npython detect.py --weights yolov3.pt --source screen\n\n# Run inference on a directory of images\npython detect.py --weights yolov3.pt --source path/to/images/\n\n# Run inference on a text file listing image paths\npython detect.py --weights yolov3.pt --source list.txt\n\n# Run inference on a text file listing stream URLs\npython detect.py --weights yolov3.pt --source list.streams\n\n# Run inference using a glob pattern for images\npython detect.py --weights yolov3.pt --source 'path/to/*.jpg'\n\n# Run inference on a YouTube video URL\npython detect.py --weights yolov3.pt --source 'https://youtu.be/LNwODJXcvt4'\n\n# Run inference on an RTSP, RTMP, or HTTP stream\npython detect.py --weights yolov3.pt --source 'rtsp://example.com/media.mp4'\n```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003eTraining\u003c/summary\u003e\n\nThe commands below show how to train YOLOv3 models on the [COCO dataset](https://docs.ultralytics.com/datasets/detect/coco/). Models and datasets are downloaded automatically. Use the largest `--batch-size` your hardware allows.\n\n```bash\n# Train YOLOv3-tiny on COCO for 300 epochs (example settings)\npython train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov3-tiny.yaml --batch-size 64\n\n# Train YOLOv3 on COCO for 300 epochs (example settings)\npython train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov3.yaml --batch-size 32\n\n# Train YOLOv3-SPP on COCO for 300 epochs (example settings)\npython train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov3-spp.yaml --batch-size 16\n```\n\n\u003c/details\u003e\n\n\u003cdetails open\u003e\n\u003csummary\u003eTutorials\u003c/summary\u003e\n\nNote: These tutorials primarily use YOLOv5 examples but the principles often apply to YOLOv3 within the Ultralytics framework.\n\n- **[Train Custom Data](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/)** 🚀 **RECOMMENDED**: Learn how to train models on your own datasets.\n- **[Tips for Best Training Results](https://docs.ultralytics.com/guides/model-training-tips/)** ☘️: Improve your model's performance with expert tips.\n- **[Multi-GPU Training](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training/)**: Speed up training using multiple GPUs.\n- **[PyTorch Hub Integration](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/)** 🌟 **NEW**: Easily load models using PyTorch Hub.\n- **[Model Export (TFLite, ONNX, CoreML, TensorRT)](https://docs.ultralytics.com/yolov5/tutorials/model_export/)** 🚀: Convert your models to various deployment formats.\n- **[NVIDIA Jetson Deployment](https://docs.ultralytics.com/guides/nvidia-jetson/)** 🌟 **NEW**: Deploy models on NVIDIA Jetson devices.\n- **[Test-Time Augmentation (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/)**: Enhance prediction accuracy with TTA.\n- **[Model Ensembling](https://docs.ultralytics.com/yolov5/tutorials/model_ensembling/)**: Combine multiple models for better performance.\n- **[Model Pruning/Sparsity](https://docs.ultralytics.com/yolov5/tutorials/model_pruning_and_sparsity/)**: Optimize models for size and speed.\n- **[Hyperparameter Evolution](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/)**: Automatically find the best training hyperparameters.\n- **[Transfer Learning with Frozen Layers](https://docs.ultralytics.com/yolov5/tutorials/transfer_learning_with_frozen_layers/)**: Adapt pretrained models to new tasks efficiently.\n- **[Architecture Summary](https://docs.ultralytics.com/yolov5/tutorials/architecture_description/)** 🌟 **NEW**: Understand the model architecture (focus on YOLOv3 principles).\n- **[Ultralytics HUB Training](https://www.ultralytics.com/hub)** 🚀 **RECOMMENDED**: Train and deploy YOLO models using Ultralytics HUB.\n- **[ClearML Logging](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration/)**: Integrate with ClearML for experiment tracking.\n- **[Neural Magic DeepSparse Integration](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization/)**: Accelerate inference with DeepSparse.\n- **[Comet Logging](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration/)** 🌟 **NEW**: Log experiments using Comet ML.\n\n\u003c/details\u003e\n\n## 🧩 Integrations\n\nUltralytics offers robust integrations with leading AI platforms to enhance your workflow, including dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with partners like [Weights \u0026 Biases](https://docs.ultralytics.com/integrations/weights-biases/), [Comet ML](https://docs.ultralytics.com/integrations/comet/), [Roboflow](https://docs.ultralytics.com/integrations/roboflow/), and [Intel OpenVINO](https://docs.ultralytics.com/integrations/openvino/), can optimize your AI projects. Explore more at [Ultralytics Integrations](https://docs.ultralytics.com/integrations/).\n\n\u003ca href=\"https://docs.ultralytics.com/integrations/\" target=\"_blank\"\u003e\n    \u003cimg width=\"100%\" src=\"https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png\" alt=\"Ultralytics active learning integrations\"\u003e\n\u003c/a\u003e\n\u003cbr\u003e\n\u003cbr\u003e\n\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://www.ultralytics.com/hub\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/partners/logo-ultralytics-hub.png\" width=\"10%\" alt=\"Ultralytics HUB logo\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"15%\" height=\"0\" alt=\"space\"\u003e\n  \u003ca href=\"https://docs.ultralytics.com/integrations/weights-biases/\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/partners/logo-wb.png\" width=\"10%\" alt=\"Weights \u0026 Biases logo\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"15%\" height=\"0\" alt=\"space\"\u003e\n  \u003ca href=\"https://docs.ultralytics.com/integrations/comet/\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png\" width=\"10%\" alt=\"Comet ML logo\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"15%\" height=\"0\" alt=\"space\"\u003e\n  \u003ca href=\"https://docs.ultralytics.com/integrations/neural-magic/\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png\" width=\"10%\" alt=\"Neural Magic logo\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n|                                                       Ultralytics HUB 🌟                                                        |                                                          Weights \u0026 Biases                                                           |                                                                              Comet                                                                              |                                                        Neural Magic                                                         |\n| :-----------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: |\n| Streamline YOLO workflows: Label, train, and deploy effortlessly with [Ultralytics HUB](https://hub.ultralytics.com/). Try now! | Track experiments, hyperparameters, and results with [Weights \u0026 Biases](https://docs.ultralytics.com/integrations/weights-biases/). | Free forever, [Comet ML](https://docs.ultralytics.com/integrations/comet/) lets you save YOLO models, resume training, and interactively visualize predictions. | Run YOLO inference up to 6x faster with [Neural Magic DeepSparse](https://docs.ultralytics.com/integrations/neural-magic/). |\n\n## ⭐ Ultralytics HUB\n\nExperience seamless AI development with [Ultralytics HUB](https://www.ultralytics.com/hub) ⭐, the ultimate platform for building, training, and deploying computer vision models. Visualize datasets, train YOLOv3, YOLOv5, and YOLOv8 🚀 models, and deploy them to real-world applications without writing any code. Transform images into actionable insights using our advanced tools and user-friendly [Ultralytics App](https://www.ultralytics.com/app-install). Start your journey for **Free** today!\n\n\u003ca align=\"center\" href=\"https://www.ultralytics.com/hub\" target=\"_blank\"\u003e\n\u003cimg width=\"100%\" src=\"https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png\" alt=\"Ultralytics HUB Platform Screenshot\"\u003e\u003c/a\u003e\n\n## 🤔 Why YOLOv3?\n\nYOLOv3 marked a major leap forward in real-time object detection at its release. Key advantages include:\n\n- **Improved Accuracy:** Enhanced detection of small objects compared to YOLOv2.\n- **Multi-Scale Predictions:** Detects objects at three different scales, boosting performance across varied object sizes.\n- **Class Prediction:** Uses logistic classifiers for object classes, enabling multi-label classification.\n- **Feature Extractor:** Employs a deeper network (Darknet-53) versus the Darknet-19 used in YOLOv2.\n\nWhile newer models like YOLOv5 and YOLO11 offer further advancements, YOLOv3 remains a reliable and widely adopted baseline, efficiently implemented in PyTorch by Ultralytics.\n\n## ☁️ Environments\n\nGet started quickly with our pre-configured environments. Click the icons below for setup details.\n\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://docs.ultralytics.com/integrations/paperspace/\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-gradient.png\" width=\"10%\" alt=\"Run on Gradient\"/\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"5%\" alt=\"\" /\u003e\n  \u003ca href=\"https://docs.ultralytics.com/integrations/google-colab/\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-colab-small.png\" width=\"10%\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"5%\" alt=\"\" /\u003e\n  \u003ca href=\"https://docs.ultralytics.com/integrations/kaggle/\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-kaggle-small.png\" width=\"10%\" alt=\"Open In Kaggle\"/\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"5%\" alt=\"\" /\u003e\n  \u003ca href=\"https://docs.ultralytics.com/guides/docker-quickstart/\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-docker-small.png\" width=\"10%\" alt=\"Docker Image\"/\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"5%\" alt=\"\" /\u003e\n  \u003ca href=\"https://docs.ultralytics.com/integrations/amazon-sagemaker/\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-aws-small.png\" width=\"10%\" alt=\"AWS Marketplace\"/\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"5%\" alt=\"\" /\u003e\n  \u003ca href=\"https://docs.ultralytics.com/integrations/google-colab/\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-gcp-small.png\" width=\"10%\" alt=\"GCP Quickstart\"/\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n## 🤝 Contribute\n\nWe welcome your contributions! Making YOLO models accessible and effective is a community effort. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started. Share your feedback through the [Ultralytics Survey](https://www.ultralytics.com/survey?utm_source=github\u0026utm_medium=social\u0026utm_campaign=Survey). Thank you to all our contributors for making Ultralytics YOLO better!\n\n[![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/yolov5/graphs/contributors)\n\n## 📜 License\n\nUltralytics provides two licensing options to meet different needs:\n\n- **AGPL-3.0 License**: An [OSI-approved](https://opensource.org/license/agpl-v3) open-source license ideal for academic research, personal projects, and testing. It promotes open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) file for details.\n- **Enterprise License**: Tailored for commercial applications, this license allows seamless integration of Ultralytics software and AI models into commercial products and services, bypassing the open-source requirements of AGPL-3.0. For commercial use cases, please contact us via [Ultralytics Licensing](https://www.ultralytics.com/license).\n\n## 📧 Contact\n\nFor bug reports and feature requests related to Ultralytics YOLO implementations, please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For general questions, discussions, and community support, join our [Discord server](https://discord.com/invite/ultralytics)!\n\n\u003cbr\u003e\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://github.com/ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png\" width=\"3%\" alt=\"Ultralytics GitHub\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\" alt=\"space\"\u003e\n  \u003ca href=\"https://www.linkedin.com/company/ultralytics/\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png\" width=\"3%\" alt=\"Ultralytics LinkedIn\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\" alt=\"space\"\u003e\n  \u003ca href=\"https://twitter.com/ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png\" width=\"3%\" alt=\"Ultralytics Twitter\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\" alt=\"space\"\u003e\n  \u003ca href=\"https://youtube.com/ultralytics?sub_confirmation=1\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png\" width=\"3%\" alt=\"Ultralytics YouTube\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\" alt=\"space\"\u003e\n  \u003ca href=\"https://www.tiktok.com/@ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png\" width=\"3%\" alt=\"Ultralytics TikTok\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\" alt=\"space\"\u003e\n  \u003ca href=\"https://ultralytics.com/bilibili\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png\" width=\"3%\" alt=\"Ultralytics BiliBili\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\" alt=\"space\"\u003e\n  \u003ca href=\"https://discord.com/invite/ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png\" width=\"3%\" alt=\"Ultralytics Discord\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fultralytics%2Fyolov3","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fultralytics%2Fyolov3","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fultralytics%2Fyolov3/lists"}