ultralytics 8.4.31

v8.4.31 - `ultralytics 8.4.31` INT8 calibration with non-square `imgsz` (#24028)

on Python PyPI

🌟 Summary

Ultralytics v8.4.31 is a reliability-focused release that mainly fixes INT8 export calibration for non-square image sizes (the headline change), while also improving training stability, export maintainability, and documentation for deployment and dataset workflows 🚀

📊 Key Changes

• 🔥 Main update (PR #24028 by @Y-T-G): INT8 calibration now works correctly with non-square imgsz

  • Fixes export calibration for commands like imgsz=640,480 with int8.
  • Affects multiple export targets: OpenVINO, TFLite, TensorRT engine, and IMX.
  • Calibration now uses the correct effective size and preserves rectangular shape during preprocessing (LetterBox behavior fixed).

• 🧩 Export system refactor (PR #23914 by @onuralpszr)

  • Export logic was split from one large file into per-format utility modules (torchscript, openvino, coreml, ncnn, mnn, paddle, rknn, axelera, etc.).
  • No major user-facing CLI change, but big internal cleanup for maintainability.

• 🍎 Apple Silicon stability improvement (PR #24038 by @Y-T-G)

  • More aggressive memory clearing on MPS devices to reduce leak-related OOM issues during train/val.

• 📦 Better auto-batch with multi-scale (PR #24051 by @glenn-jocher)

  • Auto batch-size estimation now accounts for larger effective image sizes when multi_scale is enabled.

• 📚 Docs and usability upgrades

  • New COCO-to-YOLO conversion guide (PR #23930 by @raimbekovm).
  • New YOLO26 training recipe guide (PR #23949 by @raimbekovm).
  • Platform docs now clearly explain video inference support on dedicated endpoints (PR #24029 by @t-hakobyan). 🎥

• 🛠️ CI and environment robustness

  • CI runner migration to ubuntu-latest and Codecov v6 updates.
  • Added PyTorch 2.11.0 + Torchvision 0.26.0 slow-test coverage.
  • EdgeTPU install command improved for non-interactive environments (--no-tty).

• ✅ Dataset conversion validation tightening (PR #24031 by @glenn-jocher)

  • Non-classification NDJSON→YOLO conversion now expects a val split (instead of allowing test as substitute).

🎯 Purpose & Impact

• Most important impact: users exporting INT8 models with rectangular inputs should see far fewer calibration mismatches and export failures ✅
• Deployment confidence improves across common edge/runtime formats (OpenVINO, TFLite, TensorRT, IMX) with non-square pipelines 📈
• Training becomes more reliable on Apple Silicon and with multi-scale auto-batch settings 💪
• Developer velocity increases thanks to cleaner export architecture, which should make future exporter fixes/features faster and safer 🧠
• Onboarding gets easier through clearer guides (especially COCO conversion and YOLO26 training best practices), helping both new and advanced users get productive faster 📘✨

What's Changed

• Inference video support documentation by @t-hakobyan in #24029
• Improve val availability in NDJSON to YAML by @glenn-jocher in #24031
• Add COCO to YOLO conversion guide by @raimbekovm in #23930
• Add YOLO26 training recipe guide by @raimbekovm in #23949
• Add --no-tty flag for edgetpu_compiler installation by @Laughing-q in #24032
• Bump codecov/codecov-action from 5 to 6 in /.github/workflows by @dependabot[bot] in #24034
• Add PyTorch 2.11.0 to Slow Tests CI by @lakshanthad in #24035
• refactor: split Exporter export methods into per-format utility modules by @onuralpszr in #23914
• Migrate CI to ubuntu-latest by @glenn-jocher in #24049
• Sync Platform embed card theme by @Y-T-G in #24048
• Clear MPS memory more aggressively to prevent leak by @Y-T-G in #24038
• Include multi_scale in auto-batch computation by @glenn-jocher in #24051
• ultralytics 8.4.31 INT8 calibration with non-square imgsz by @Y-T-G in #24028

Full Changelog: v8.4.30...v8.4.31