ultralytics 8.4.80

v8.4.80 - Replace `int8` and `half` args with new `quantize` (#24918)

on Python PyPI

🌟 Summary

🚀 Ultralytics v8.4.80 is mainly about making model export precision much simpler and more future-ready, with a new unified quantize argument replacing the older half and int8 switches—plus a few important reliability, validation, and training stability fixes.

📊 Key Changes

• New unified quantize export argument by @onuralpszr ⭐

  • Replaces the older half=True and int8=True style with one cleaner setting.
  • Supports simple values like:
    • quantize=16 for FP16
    • quantize=8 for INT8
    • quantize=32 for FP32
  • Also supports more advanced mixed-precision schemes like quantize="w8a16" where supported.
  • Older half and int8 arguments still work for now, so existing scripts should not break immediately. 🔄

• Export docs and integrations updated across the board

  • Many export examples and docs were refreshed to use quantize instead of half/int8.
  • This affects formats and platforms such as ONNX, TensorRT, OpenVINO, CoreML, TFLite, TF.js, RKNN, QNN, Axelera, DEEPX, and more. 📦

• Better support for modern and future quantization workflows

  • The new system is designed to scale beyond just “FP16 or INT8”.
  • This is especially useful as more hardware targets adopt mixed precision and platform-specific quantization modes. ⚙️

• Distributed validation now combines confusion matrices across GPUs

  • In multi-GPU validation, confusion matrix plots now reflect results from all workers, not just one process. 📈

• OBB training stability improved

  • A fix prevents some very tiny oriented bounding boxes from causing unstable or invalid loss behavior, including NaNs during training. 🛡️

• HUB/network request reliability improved

  • Added real request timeouts and retry behavior so stalled network calls are less likely to hang indefinitely. 🌐

• MLflow integration tests restored and modernized

  • Improves confidence that MLflow logging behavior continues to work correctly with newer MLflow versions. 🧪

• Tracking docs clarified

  • Better explanation of when to use persist=True and how to choose tracker backends like botsort.yaml or bytetrack.yaml. 🎯

• Ultralytics Platform dataset upload docs improved

  • Clearer explanation that browser video uploads depend on codec support, not just file extension.
  • AVI was removed from supported browser-upload guidance. 🎥

🎯 Purpose & Impact

• Simpler exporting for everyone 😌

  • Instead of remembering two separate boolean flags, users now have one clearer precision setting: quantize.
  • This makes export commands easier to read, teach, automate, and maintain.

• More future-proof deployment workflows 🔮

  • The old half/int8 flags were too limited for newer precision schemes.
  • The new design opens the door to richer hardware-specific export options without adding more confusing flags later.

• Safer upgrade path ✅

  • Backward compatibility is preserved, so current projects using half or int8 should keep working while users gradually migrate.

• Better results in multi-GPU setups 🖥️

  • Teams validating models across multiple GPUs should now get more accurate confusion matrix visualizations.

• More stable training for OBB models 🎯

  • Users working with oriented bounding boxes, especially small objects, may see fewer training failures and more reliable optimization.

• Fewer frustrating network hangs ⏱️

  • HUB-related actions and downloads should fail more gracefully instead of getting stuck indefinitely.

• Improved docs and onboarding 📚

  • Several updates make deployment, tracking, segmentation guides, and Platform uploads easier to understand for both new and experienced users.

Overall, v8.4.80 is a quality-of-life and deployment-focused release: not a flashy new model drop, but a meaningful improvement for anyone exporting YOLO models to production 🚀

What's Changed

• Clarify tracking persistence and tracker selection by @joeydufourd in #24889
• Restore MLflow integration tests on modern mlflow by @raimbekovm in #24728
• Gather confusion matrix across GPUs in DDP validation by @Y-T-G in #24803
• Forward socket timeout in hub smart_request by @raimbekovm in #24743
• Fix double assignment and destructive pop in isolating segmentation guide by @raimbekovm in #24701
• Document browser video codec support for Platform dataset uploads by @laodouya in #24802
• Add timeout to requests calls in Hub and downloads by @raimbekovm in #24734
• Fix NaN OBB loss due to sub-stride boxes by @Y-T-G in #24933
• Replace int8 and half args with new quantize by @onuralpszr in #24918

New Contributors

• @joeydufourd made their first contribution in #24889

Full Changelog: v8.4.79...v8.4.80