ultralytics 8.4.46

v8.4.46 - `ultralytics 8.4.46` Fix `multiscale` minimum train size (#24394)

on Python PyPI

🌟 Summary

Ultralytics v8.4.46 is a stability-focused release that primarily fixes a key multi-scale training edge case 🔧, while also improving export reliability, hardware support clarity, and documentation quality across YOLO workflows.

📊 Key Changes

• 🚨 Priority fix (PR #24394 by @glenn-jocher): Multi-scale training minimum size clamp

  • Multi-scale random resizing now enforces a safe lower bound of at least one model stride.
  • Added a regression test to prevent future breakage when training with aggressive shrinking on small images.
  • Version bumped from 8.4.45 → 8.4.46.

• 🔁 Resume training safety fix (PR #24386 by @lmycross)

  • Resuming from an already finished checkpoint now correctly stops with “nothing to resume,” instead of running an extra epoch.

• 📦 Export usability and correctness improvements

  • Export completion logs now show the exact artifact path (not just parent folder) (PR #24316).
  • Export argument support was aligned across code/docs for several formats, especially around data and fraction for INT8 calibration (PR #24382).

• 🧠 RKNN support clarified and enforced (PR #24384 by @lakshanthad)

  • RKNN export now clearly blocks INT8-only Rockchip targets (rv1103, rv1106, rv1103b, rv1106b) with a helpful error.
  • Docs now explicitly reflect current RKNN export behavior: FP16-supported targets only.

• 📚 Docs and content polish

  • Fixed strict docs validation warnings and anchor issues (PR #24389).
  • Refreshed tutorial videos/captions in dataset and Streamlit docs for clearer onboarding (PR #24370 + related doc updates).

🎯 Purpose & Impact

• More robust training 🛡️
  The multi-scale clamp fix prevents invalid tiny image sizes, reducing crash risk and instability during training—especially for small-image or aggressive augmentation setups.

• Less wasted compute/time ⏱️
  The resume fix avoids accidental extra epochs when training is already complete.

• Cleaner deployment experience 🚀
  Better export path reporting and clearer per-format argument support make exporting easier to automate and debug.

• Fewer hardware surprises 🤝
  RKNN users now get immediate, explicit feedback on unsupported INT8-only chips, avoiding confusing late-stage failures.

• Better docs trustworthiness ✅
  Improved documentation consistency helps both new and advanced users move faster with fewer mismatches between docs and actual behavior.

What's Changed

• Improve RKNN exports to highlight non-int8 supported chips by @lakshanthad in #24384
• Fix: stop resume when training is already finished by @lmycross in #24386
• Add https://youtu.be/FvWl00sD4rc to docs by @RizwanMunawar in #24370
• Improve data and fraction args across export formats by @lakshanthad in #24382
• Improve export results path to point to actual artifact path by @lakshanthad in #24316
• Fix docs strict validation warnings by @glenn-jocher in #24389
• ultralytics 8.4.46 Fix multiscale minimum train size by @glenn-jocher in #24394

Full Changelog: v8.4.45...v8.4.46