ultralytics 8.4.67

v8.4.67 - Add opt-in weights_only model loading via ULTRALYTICS_SAFE_LOAD (#24829)

on Python PyPI

🌟 Summary

v8.4.67 is a small but meaningful release focused on safer model loading 🔒, plus an important TT100K dataset label fix 🛣️ and a batch of documentation/link cleanups 📚.

📊 Key Changes

• 🔒 New opt-in safe model loading via ULTRALYTICS_SAFE_LOAD (@glenn-jocher, PR #24829)

  • Ultralytics added a new environment flag, ULTRALYTICS_SAFE_LOAD=true, that enables a safer way to load model checkpoints.
  • When enabled, checkpoint loading uses PyTorch’s newer weights_only=True behavior and only rebuilds known Ultralytics and PyTorch model classes from an automatically generated allow-list.
  • This replaces the older unused SafeUnpickler approach.
  • Important: this is off by default, so existing workflows keep working as before.

• 🛣️ TT100K dataset class list corrected to the official 221-category set (@glenn-jocher, PR #24718)

  • The TT100K.yaml dataset config had duplicated and incorrect class names.
  • That caused some class IDs to silently lose labels during conversion, even though training still proceeded.
  • The release now aligns TT100K with the official upstream category list, including missing real classes.

• 📘 Rust inference docs updated to ultralytics-inference 0.0.21 (@onuralpszr, PR #24825)

  • Documentation examples now point to the latest Rust inference crate version.

• 🔗 Many documentation links were refreshed and corrected (@glenn-jocher, PR #24824 and #24817)

  • Updated links across integrations, datasets, examples, and guides.
  • Includes a fix for the CoreML NMS glossary link and many canonical URL updates.

🎯 Purpose & Impact

• 🔐 Safer checkpoint loading for security-conscious users

  • The new opt-in safe loading mode helps reduce risk when loading model files by allowing only recognized model components.
  • This is especially useful for teams working in stricter environments or aligning with newer PyTorch defaults.
  • Because it is optional, users with custom or forked architectures are not disrupted.

• ♻️ Better future compatibility with PyTorch

  • PyTorch 2.6+ is moving toward weights_only loading by default.
  • This update helps Ultralytics stay aligned with that direction while preserving backward compatibility today.

• 🚫 Less chance of silent dataset errors

  • The TT100K fix is a big quality improvement for anyone training on that traffic-sign dataset.
  • Previously, some categories could effectively receive no labels without an obvious crash.
  • Now, dataset conversion and training should be much more trustworthy 🎯

• 🧰 Minimal disruption for most users

  • No major model architecture changes were introduced in this tag.
  • Standard loading behavior remains unchanged unless you explicitly enable safe loading.
  • Most other changes are documentation polish, so the release is low-risk and practical ✅

• 📚 Clearer onboarding and fewer broken docs

  • Updated links and version references make tutorials and integration guides easier to follow, especially for newer users.

If you want, I can also provide a one-paragraph release note version or a developer-focused summary of v8.4.67.

What's Changed

• Fix CoreML NMS glossary link by @glenn-jocher in #24817
• Fix TT100K class names to match the official 221-category list by @glenn-jocher in #24718
• chore: update ultralytics-inference version to 0.0.21 in documentation by @onuralpszr in #24825
• Update redirected docs links by @glenn-jocher in #24824
• Add opt-in weights_only model loading via ULTRALYTICS_SAFE_LOAD by @glenn-jocher in #24829

Full Changelog: v8.4.66...v8.4.67