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ultralytics/yolov5

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cached 2026-03-30T12:53:22.880Z
3mo ago

ultralytics/yolov5

ultralytics/yolov5 is a widely used Python/PyTorch computer-vision repository for YOLOv5, with support for object detection, image segmentation, image classification, and exports to ONNX/CoreML/TFLite. It is active, not archived, and has very large adoption signals: 57,108 stars, 17,449 forks, and a recent push on 2026-03-18.

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Stars57,108
Forks17,449
Default branchmaster
Last pushed2026-03-18T16:08:22Z
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Prefer this fork if localization and fork-specific workflow edits matter more than staying current with upstream. Prefer upstream if you want the latest fixes, cleaner export parity, and lower maintenance risk.

Choose this fork if you need Rockchip-tuned YOLOv5 behavior and can absorb divergence from upstream. Choose upstream if you want current fixes, standard tutorials, and the broadest compatibility.

Choose this fork if your priority is mobile deployment and you can tolerate an older, heavily diverged codebase. Choose upstream if you want the current, broadly maintained YOLOv5 with the full standard training/export workflow.

Choose this fork only if its custom optimization/demo packaging is exactly what you need. For most users, upstream is the safer default because it is far more active, broader in capability, and much less likely to be missing important fixes or workflows.

Choose this fork if RKNN deployment is the goal. Choose upstream if you want a current, general-purpose YOLOv5 codebase with broader export options, fresher maintenance, and less migration risk.

Choose this fork if your goal is detector knowledge distillation on top of YOLOv5. Choose upstream if you need the latest general-purpose YOLOv5 features, active maintenance, and lower operational risk.

Prefer this fork if your goal is adversarial-patch research on YOLOv5. Prefer upstream if you need the broadest, cleanest, and most current general-purpose YOLOv5 codebase.

Prefer this fork only if its distillation and OpenVINO-specific changes are the point. If you want current YOLOv5 functionality, active maintenance, or broad upstream compatibility, upstream is the safer choice.

Prefer this fork if you want a specialized military-vehicle detector and value packaged training assets over upstream freshness. Prefer upstream if you need current maintenance, broader task support, or a generic YOLOv5 foundation.

Choose this fork only if you want a specialized emotion-detection branch and can live without much of upstream YOLOv5’s broader training, export, classification, and segmentation surface. For general YOLOv5 work or long-term maintenance, upstream is the safer default.