[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9665":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":16,"forks30d":16,"starsTrendScore":19,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":24,"hasPages":24,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":31,"readmeContent":32,"aiSummary":33,"trendingCount":16,"starSnapshotCount":16,"syncStatus":17,"lastSyncTime":34,"discoverSource":35},9665,"yolov3","ultralytics\u002Fyolov3","ultralytics","Ultralytics YOLOv3 in PyTorch > ONNX > CoreML > TFLite","https:\u002F\u002Fdocs.ultralytics.com",null,"Python",10569,3432,148,3,0,2,11,1,72.1,"GNU Affero General Public License v3.0",false,"master",true,[26,27,28,7,29,5,30],"deep-learning","machine-learning","object-detection","yolo","yolov5","2026-06-12 04:00:46","\u003Cdiv align=\"center\">\n  \u003Cp>\n    \u003Ca href=\"https:\u002F\u002Fplatform.ultralytics.com\u002F?utm_source=github&utm_medium=referral&utm_campaign=platform_launch&utm_content=banner&utm_term=ultralytics_github\" target=\"_blank\">\n      \u003Cimg width=\"100%\" src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fultralytics\u002Fassets\u002Fmain\u002Fyolov3\u002Fbanner-yolov3.png\" alt=\"Ultralytics YOLOv3 banner\">\u003C\u002Fa>\n  \u003C\u002Fp>\n\n[中文](https:\u002F\u002Fdocs.ultralytics.com\u002Fzh\u002F) | [한국어](https:\u002F\u002Fdocs.ultralytics.com\u002Fko\u002F) | [日本語](https:\u002F\u002Fdocs.ultralytics.com\u002Fja\u002F) | [Русский](https:\u002F\u002Fdocs.ultralytics.com\u002Fru\u002F) | [Deutsch](https:\u002F\u002Fdocs.ultralytics.com\u002Fde\u002F) | [Français](https:\u002F\u002Fdocs.ultralytics.com\u002Ffr\u002F) | [Español](https:\u002F\u002Fdocs.ultralytics.com\u002Fes) | [Português](https:\u002F\u002Fdocs.ultralytics.com\u002Fpt\u002F) | [Türkçe](https:\u002F\u002Fdocs.ultralytics.com\u002Ftr\u002F) | [Tiếng Việt](https:\u002F\u002Fdocs.ultralytics.com\u002Fvi\u002F) | [العربية](https:\u002F\u002Fdocs.ultralytics.com\u002Far\u002F)\n\n\u003Cdiv>\n    \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fyolov3\u002Factions\u002Fworkflows\u002Fci-testing.yml\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fyolov3\u002Factions\u002Fworkflows\u002Fci-testing.yml\u002Fbadge.svg\" alt=\"YOLOv3 CI\">\u003C\u002Fa>\n    \u003Ca href=\"https:\u002F\u002Fzenodo.org\u002Fbadge\u002Flatestdoi\u002F264818686\">\u003Cimg src=\"https:\u002F\u002Fzenodo.org\u002Fbadge\u002F264818686.svg\" alt=\"YOLOv3 Citation\">\u003C\u002Fa>\n    \u003Ca href=\"https:\u002F\u002Fhub.docker.com\u002Fr\u002Fultralytics\u002Fyolov3\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fdocker\u002Fpulls\u002Fultralytics\u002Fyolov3?logo=docker\" alt=\"Docker Pulls\">\u003C\u002Fa>\n    \u003Ca href=\"https:\u002F\u002Fdiscord.com\u002Finvite\u002Fultralytics\">\u003Cimg alt=\"Discord\" src=\"https:\u002F\u002Fimg.shields.io\u002Fdiscord\u002F1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue\">\u003C\u002Fa>\n    \u003Ca href=\"https:\u002F\u002Fcommunity.ultralytics.com\u002F\">\u003Cimg alt=\"Ultralytics Forums\" src=\"https:\u002F\u002Fimg.shields.io\u002Fdiscourse\u002Fusers?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue\">\u003C\u002Fa>\n    \u003Ca href=\"https:\u002F\u002Fwww.reddit.com\u002Fr\u002Fultralytics\u002F\">\u003Cimg alt=\"Ultralytics Reddit\" src=\"https:\u002F\u002Fimg.shields.io\u002Freddit\u002Fsubreddit-subscribers\u002Fultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue\">\u003C\u002Fa>\n    \u003Cbr>\n    \u003Ca href=\"https:\u002F\u002Fbit.ly\u002Fyolov5-paperspace-notebook\">\u003Cimg src=\"https:\u002F\u002Fassets.paperspace.io\u002Fimg\u002Fgradient-badge.svg\" alt=\"Run on Gradient\">\u003C\u002Fa>\n    \u003Ca href=\"https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fultralytics\u002Fyolov5\u002Fblob\u002Fmaster\u002Ftutorial.ipynb\">\u003Cimg src=\"https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\" alt=\"Open In Colab\">\u003C\u002Fa>\n    \u003Ca href=\"https:\u002F\u002Fwww.kaggle.com\u002Fmodels\u002Fultralytics\u002Fyolov5\">\u003Cimg src=\"https:\u002F\u002Fkaggle.com\u002Fstatic\u002Fimages\u002Fopen-in-kaggle.svg\" alt=\"Open In Kaggle\">\u003C\u002Fa>\n  \u003C\u002Fdiv>\n  \u003Cbr>\n\nUltralytics YOLOv3 is a robust and efficient [computer vision](https:\u002F\u002Fwww.ultralytics.com\u002Fglossary\u002Fcomputer-vision-cv) model developed by [Ultralytics](https:\u002F\u002Fwww.ultralytics.com\u002F). Built on the [PyTorch](https:\u002F\u002Fpytorch.org\u002F) framework, this implementation extends the original YOLOv3 architecture, renowned for its improvements in [object detection](https:\u002F\u002Fwww.ultralytics.com\u002Fglossary\u002Fobject-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:\u002F\u002Fdocs.ultralytics.com\u002F) for in-depth guidance (YOLOv3-specific docs may be limited, but general YOLO principles apply), open an issue on [GitHub](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fyolov5\u002Fissues\u002Fnew\u002Fchoose) for support, and join our [Discord community](https:\u002F\u002Fdiscord.com\u002Finvite\u002Fultralytics) for questions and discussions!\n\nFor Enterprise License requests, please complete the form at [Ultralytics Licensing](https:\u002F\u002Fwww.ultralytics.com\u002Flicense).\n\n\u003Cdiv align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fultralytics\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-github.png\" width=\"2%\" alt=\"Ultralytics GitHub\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"2%\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fultralytics\u002F\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-linkedin.png\" width=\"2%\" alt=\"Ultralytics LinkedIn\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"2%\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Ftwitter.com\u002Fultralytics\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-twitter.png\" width=\"2%\" alt=\"Ultralytics Twitter\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"2%\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fwww.youtube.com\u002Fultralytics?sub_confirmation=1\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-youtube.png\" width=\"2%\" alt=\"Ultralytics YouTube\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"2%\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fwww.tiktok.com\u002F@ultralytics\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-tiktok.png\" width=\"2%\" alt=\"Ultralytics TikTok\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"2%\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fultralytics.com\u002Fbilibili\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-bilibili.png\" width=\"2%\" alt=\"Ultralytics BiliBili\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"2%\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fdiscord.com\u002Finvite\u002Fultralytics\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-discord.png\" width=\"2%\" alt=\"Ultralytics Discord\">\u003C\u002Fa>\n\u003C\u002Fdiv>\n\u003C\u002Fdiv>\n\u003Cbr>\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:\u002F\u002Fgithub.com\u002Fultralytics\u002Fultralytics), YOLO11 continues our legacy of speed, precision, and user-friendly design. Whether you're working on [object detection](https:\u002F\u002Fdocs.ultralytics.com\u002Ftasks\u002Fdetect\u002F), [instance segmentation](https:\u002F\u002Fdocs.ultralytics.com\u002Ftasks\u002Fsegment\u002F), [pose estimation](https:\u002F\u002Fdocs.ultralytics.com\u002Ftasks\u002Fpose\u002F), [image classification](https:\u002F\u002Fdocs.ultralytics.com\u002Ftasks\u002Fclassify\u002F), or [oriented object detection (OBB)](https:\u002F\u002Fdocs.ultralytics.com\u002Ftasks\u002Fobb\u002F), 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:\u002F\u002Fdocs.ultralytics.com\u002F) for comprehensive guides and resources:\n\n[![PyPI version](https:\u002F\u002Fbadge.fury.io\u002Fpy\u002Fultralytics.svg)](https:\u002F\u002Fbadge.fury.io\u002Fpy\u002Fultralytics) [![Downloads](https:\u002F\u002Fstatic.pepy.tech\u002Fbadge\u002Fultralytics)](https:\u002F\u002Fpepy.tech\u002Fprojects\u002Fultralytics)\n\n```bash\n# Install the ultralytics package\npip install ultralytics\n```\n\n\u003Cdiv align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fwww.ultralytics.com\u002Fyolo\" target=\"_blank\">\n  \u003Cimg width=\"100%\" src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fultralytics\u002Fassets\u002Frefs\u002Fheads\u002Fmain\u002Fyolo\u002Fperformance-comparison.png\" alt=\"Ultralytics YOLO Performance Comparison\">\u003C\u002Fa>\n\u003C\u002Fdiv>\n\n## 📚 Documentation\n\nSee the [Ultralytics Docs for YOLOv3](https:\u002F\u002Fdocs.ultralytics.com\u002Fmodels\u002Fyolov3\u002F) 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>\n\u003Csummary>Install\u003C\u002Fsummary>\n\nClone the repository and install dependencies from `requirements.txt` in a [**Python>=3.8.0**](https:\u002F\u002Fwww.python.org\u002F) environment. Ensure you have [**PyTorch>=1.8**](https:\u002F\u002Fpytorch.org\u002Fget-started\u002Flocally\u002F) 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:\u002F\u002Fgithub.com\u002Fultralytics\u002Fyolov3\n\n# Navigate to the cloned directory\ncd yolov3\n\n# Install required packages\npip install -r requirements.txt\n```\n\n\u003C\u002Fdetails>\n\n\u003Cdetails open>\n\u003Csummary>Inference with PyTorch Hub\u003C\u002Fsummary>\n\nUse YOLOv3 via [PyTorch Hub](https:\u002F\u002Fdocs.ultralytics.com\u002Fyolov5\u002Ftutorials\u002Fpytorch_hub_model_loading\u002F) for inference. [Models](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fyolov5\u002Ftree\u002Fmaster\u002Fmodels) 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\u002Fyolov3\", \"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:\u002F\u002Fultralytics.com\u002Fimages\u002Fzidane.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\u002Fdetect\u002Fexp\n```\n\n\u003C\u002Fdetails>\n\n\u003Cdetails>\n\u003Csummary>Inference with detect.py\u003C\u002Fsummary>\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\u002Fdetect`.\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\u002Fto\u002Fimages\u002F\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\u002Fto\u002F*.jpg'\n\n# Run inference on a YouTube video URL\npython detect.py --weights yolov3.pt --source 'https:\u002F\u002Fyoutu.be\u002FLNwODJXcvt4'\n\n# Run inference on an RTSP, RTMP, or HTTP stream\npython detect.py --weights yolov3.pt --source 'rtsp:\u002F\u002Fexample.com\u002Fmedia.mp4'\n```\n\n\u003C\u002Fdetails>\n\n\u003Cdetails>\n\u003Csummary>Training\u003C\u002Fsummary>\n\nThe commands below show how to train YOLOv3 models on the [COCO dataset](https:\u002F\u002Fdocs.ultralytics.com\u002Fdatasets\u002Fdetect\u002Fcoco\u002F). 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\u002Fdetails>\n\n\u003Cdetails open>\n\u003Csummary>Tutorials\u003C\u002Fsummary>\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:\u002F\u002Fdocs.ultralytics.com\u002Fyolov5\u002Ftutorials\u002Ftrain_custom_data\u002F)** 🚀 **RECOMMENDED**: Learn how to train models on your own datasets.\n- **[Tips for Best Training Results](https:\u002F\u002Fdocs.ultralytics.com\u002Fguides\u002Fmodel-training-tips\u002F)** ☘️: Improve your model's performance with expert tips.\n- **[Multi-GPU Training](https:\u002F\u002Fdocs.ultralytics.com\u002Fyolov5\u002Ftutorials\u002Fmulti_gpu_training\u002F)**: Speed up training using multiple GPUs.\n- **[PyTorch Hub Integration](https:\u002F\u002Fdocs.ultralytics.com\u002Fyolov5\u002Ftutorials\u002Fpytorch_hub_model_loading\u002F)** 🌟 **NEW**: Easily load models using PyTorch Hub.\n- **[Model Export (TFLite, ONNX, CoreML, TensorRT)](https:\u002F\u002Fdocs.ultralytics.com\u002Fyolov5\u002Ftutorials\u002Fmodel_export\u002F)** 🚀: Convert your models to various deployment formats.\n- **[NVIDIA Jetson Deployment](https:\u002F\u002Fdocs.ultralytics.com\u002Fguides\u002Fnvidia-jetson\u002F)** 🌟 **NEW**: Deploy models on NVIDIA Jetson devices.\n- **[Test-Time Augmentation (TTA)](https:\u002F\u002Fdocs.ultralytics.com\u002Fyolov5\u002Ftutorials\u002Ftest_time_augmentation\u002F)**: Enhance prediction accuracy with TTA.\n- **[Model Ensembling](https:\u002F\u002Fdocs.ultralytics.com\u002Fyolov5\u002Ftutorials\u002Fmodel_ensembling\u002F)**: Combine multiple models for better performance.\n- **[Model Pruning\u002FSparsity](https:\u002F\u002Fdocs.ultralytics.com\u002Fyolov5\u002Ftutorials\u002Fmodel_pruning_and_sparsity\u002F)**: Optimize models for size and speed.\n- **[Hyperparameter Evolution](https:\u002F\u002Fdocs.ultralytics.com\u002Fyolov5\u002Ftutorials\u002Fhyperparameter_evolution\u002F)**: Automatically find the best training hyperparameters.\n- **[Transfer Learning with Frozen Layers](https:\u002F\u002Fdocs.ultralytics.com\u002Fyolov5\u002Ftutorials\u002Ftransfer_learning_with_frozen_layers\u002F)**: Adapt pretrained models to new tasks efficiently.\n- **[Architecture Summary](https:\u002F\u002Fdocs.ultralytics.com\u002Fyolov5\u002Ftutorials\u002Farchitecture_description\u002F)** 🌟 **NEW**: Understand the model architecture (focus on YOLOv3 principles).\n- **[Ultralytics Platform Training](https:\u002F\u002Fplatform.ultralytics.com)** 🚀 **RECOMMENDED**: Train and deploy YOLO models using Ultralytics Platform.\n- **[ClearML Logging](https:\u002F\u002Fdocs.ultralytics.com\u002Fyolov5\u002Ftutorials\u002Fclearml_logging_integration\u002F)**: Integrate with ClearML for experiment tracking.\n- **[Neural Magic DeepSparse Integration](https:\u002F\u002Fdocs.ultralytics.com\u002Fyolov5\u002Ftutorials\u002Fneural_magic_pruning_quantization\u002F)**: Accelerate inference with DeepSparse.\n- **[Comet Logging](https:\u002F\u002Fdocs.ultralytics.com\u002Fyolov5\u002Ftutorials\u002Fcomet_logging_integration\u002F)** 🌟 **NEW**: Log experiments using Comet ML.\n\n\u003C\u002Fdetails>\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 & Biases](https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Fweights-biases\u002F), [Comet ML](https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Fcomet\u002F), [Roboflow](https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Froboflow\u002F), and [Intel OpenVINO](https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Fopenvino\u002F), can optimize your AI projects. Explore more at [Ultralytics Integrations](https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002F).\n\n\u003Ca href=\"https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002F\" target=\"_blank\">\n    \u003Cimg width=\"100%\" src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fyolov8\u002Fbanner-integrations.png\" alt=\"Ultralytics active learning integrations\">\n\u003C\u002Fa>\n\u003Cbr>\n\u003Cbr>\n\n\u003Cdiv align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fplatform.ultralytics.com\">\n    \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fpartners\u002Flogo-ultralytics-hub.png\" width=\"10%\" alt=\"Ultralytics Platform logo\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"15%\" height=\"0\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Fweights-biases\u002F\">\n    \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fpartners\u002Flogo-wb.png\" width=\"10%\" alt=\"Weights & Biases logo\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"15%\" height=\"0\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Fcomet\u002F\">\n    \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fpartners\u002Flogo-comet.png\" width=\"10%\" alt=\"Comet ML logo\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"15%\" height=\"0\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Fneural-magic\u002F\">\n    \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fpartners\u002Flogo-neuralmagic.png\" width=\"10%\" alt=\"Neural Magic logo\">\u003C\u002Fa>\n\u003C\u002Fdiv>\n\n|                                                         Ultralytics Platform 🌟                                                          |                                                          Weights & Biases                                                           |                                                                              Comet                                                                              |                                                        Neural Magic                                                         |\n| :--------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: |\n| Streamline YOLO workflows: Label, train, and deploy effortlessly with [Ultralytics Platform](https:\u002F\u002Fplatform.ultralytics.com). Try now! | Track experiments, hyperparameters, and results with [Weights & Biases](https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Fweights-biases\u002F). | Free forever, [Comet ML](https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Fcomet\u002F) lets you save YOLO models, resume training, and interactively visualize predictions. | Run YOLO inference up to 6x faster with [Neural Magic DeepSparse](https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Fneural-magic\u002F). |\n\n## ⭐ Ultralytics Platform\n\nExperience seamless AI development with [Ultralytics Platform](https:\u002F\u002Fplatform.ultralytics.com) ⭐, 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:\u002F\u002Fwww.ultralytics.com\u002Fapp-install). Start your journey for **Free** today!\n\n\u003Ca align=\"center\" href=\"https:\u002F\u002Fplatform.ultralytics.com\" target=\"_blank\">\n\u003Cimg width=\"100%\" src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fim\u002Fultralytics-hub.png\" alt=\"Ultralytics Platform Platform Screenshot\">\u003C\u002Fa>\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\">\n  \u003Ca href=\"https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Fpaperspace\u002F\">\n    \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv0.0.0\u002Flogo-gradient.png\" width=\"10%\" alt=\"Run on Gradient\"\u002F>\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"5%\" alt=\"\" \u002F>\n  \u003Ca href=\"https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Fgoogle-colab\u002F\">\n    \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv0.0.0\u002Flogo-colab-small.png\" width=\"10%\" alt=\"Open In Colab\"\u002F>\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"5%\" alt=\"\" \u002F>\n  \u003Ca href=\"https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Fkaggle\u002F\">\n    \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv0.0.0\u002Flogo-kaggle-small.png\" width=\"10%\" alt=\"Open In Kaggle\"\u002F>\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"5%\" alt=\"\" \u002F>\n  \u003Ca href=\"https:\u002F\u002Fdocs.ultralytics.com\u002Fguides\u002Fdocker-quickstart\u002F\">\n    \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv0.0.0\u002Flogo-docker-small.png\" width=\"10%\" alt=\"Docker Image\"\u002F>\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"5%\" alt=\"\" \u002F>\n  \u003Ca href=\"https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Famazon-sagemaker\u002F\">\n    \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv0.0.0\u002Flogo-aws-small.png\" width=\"10%\" alt=\"AWS Marketplace\"\u002F>\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"5%\" alt=\"\" \u002F>\n  \u003Ca href=\"https:\u002F\u002Fdocs.ultralytics.com\u002Fintegrations\u002Fgoogle-colab\u002F\">\n    \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Freleases\u002Fdownload\u002Fv0.0.0\u002Flogo-gcp-small.png\" width=\"10%\" alt=\"GCP Quickstart\"\u002F>\u003C\u002Fa>\n\u003C\u002Fdiv>\n\n## 🤝 Contribute\n\nWe welcome your contributions! Making YOLO models accessible and effective is a community effort. Please see our [Contributing Guide](https:\u002F\u002Fdocs.ultralytics.com\u002Fhelp\u002Fcontributing\u002F) to get started. Share your feedback through the [Ultralytics Survey](https:\u002F\u002Fwww.ultralytics.com\u002Fsurvey?utm_source=github&utm_medium=social&utm_campaign=Survey). Thank you to all our contributors for making Ultralytics YOLO better!\n\n[![Ultralytics open-source contributors](https:\u002F\u002Fraw.githubusercontent.com\u002Fultralytics\u002Fassets\u002Fmain\u002Fim\u002Fimage-contributors.png)](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fyolov5\u002Fgraphs\u002Fcontributors)\n\n## 📜 License\n\nUltralytics provides two licensing options to meet different needs:\n\n- **AGPL-3.0 License**: An [OSI-approved](https:\u002F\u002Fopensource.org\u002Flicense\u002Fagpl-3.0) open-source license ideal for academic research, personal projects, and testing. It promotes open collaboration and knowledge sharing. See the [LICENSE](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fyolov3\u002Fblob\u002Fmaster\u002FLICENSE) 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:\u002F\u002Fwww.ultralytics.com\u002Flicense).\n\n## 📧 Contact\n\nFor bug reports and feature requests related to Ultralytics YOLO implementations, please visit [GitHub Issues](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fyolov5\u002Fissues). For general questions, discussions, and community support, join our [Discord server](https:\u002F\u002Fdiscord.com\u002Finvite\u002Fultralytics)!\n\n\u003Cbr>\n\u003Cdiv align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fultralytics\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-github.png\" width=\"3%\" alt=\"Ultralytics GitHub\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"3%\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fultralytics\u002F\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-linkedin.png\" width=\"3%\" alt=\"Ultralytics LinkedIn\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"3%\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Ftwitter.com\u002Fultralytics\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-twitter.png\" width=\"3%\" alt=\"Ultralytics Twitter\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"3%\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fwww.youtube.com\u002Fultralytics?sub_confirmation=1\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-youtube.png\" width=\"3%\" alt=\"Ultralytics YouTube\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"3%\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fwww.tiktok.com\u002F@ultralytics\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-tiktok.png\" width=\"3%\" alt=\"Ultralytics TikTok\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"3%\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fultralytics.com\u002Fbilibili\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-bilibili.png\" width=\"3%\" alt=\"Ultralytics BiliBili\">\u003C\u002Fa>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"3%\" alt=\"space\">\n  \u003Ca href=\"https:\u002F\u002Fdiscord.com\u002Finvite\u002Fultralytics\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fassets\u002Fraw\u002Fmain\u002Fsocial\u002Flogo-social-discord.png\" width=\"3%\" alt=\"Ultralytics Discord\">\u003C\u002Fa>\n\u003C\u002Fdiv>\n","Ultralytics YOLOv3 是一个基于 PyTorch 的高效计算机视觉模型，主要用于目标检测。该项目不仅支持 PyTorch 框架下的训练与推理，还提供了向 ONNX、CoreML 和 TFLite 等多种格式的转换能力，使得模型可以更广泛地应用于不同平台和设备上。YOLOv3 以其在速度与精度上的优秀表现而闻名，特别适合需要实时处理大量图像数据的应用场景，如视频监控、自动驾驶以及移动设备上的图像识别任务。通过采用先进的深度学习技术，Ultralytics YOLOv3 能够为开发者提供强大的工具来构建高性能的目标检测系统。","2026-06-11 03:24:03","top_topic"]