ultralytics/ultralytics v8.4.156

v8.4.156 - Refresh remote NDJSON at the conversion owner (#26242)

on GitHub

🌟 Summary

v8.4.156 improves remote NDJSON dataset reliability and makes INT8 TensorRT exports faster while preserving accuracy. 🚀

📊 Key Changes

• 🔄 Reliable remote NDJSON conversion — PR #26242 by @cainiao33

  • Refreshes the remote NDJSON manifest inside the existing conversion lock, preventing stale dataset manifests from being silently reused.
  • Uses a private temporary download directory so similarly named manifests from different URLs cannot overwrite one another.
  • Keeps the normalized content hash as the conversion cache:
    • Unchanged datasets—including those with rotated signed URLs—reuse existing labels and images without unnecessary downloads.
    • Changed manifests generate a new dataset directory, preserving files used by active training jobs.
  • Removes NDJSON-specific deletion behavior from the general check_file utility, making file handling more predictable.
  • Validated across detection, classification, depth, local sources, concurrent downloads, interrupted conversions, and URL basename collisions.

• ⚡ Faster and more accurate INT8 TensorRT exports — PR #26171 by @Y-T-G

  • QAT now quantizes more of the model head while keeping accuracy-sensitive final output layers and DFL operations in floating point.
  • BatchNorm layers are fused during export, prediction, and validation, reducing runtime overhead.
  • Quantization ranges are adjusted when BatchNorm is fused so INT8 values remain effectively unchanged.
  • PTQ exports now keep the most accuracy-sensitive head operations out of INT8 and use FP16 where appropriate.
  • TensorRT precision handling was refined for both newer and older TensorRT versions.

• 🧪 More robust testing — PR #26217 by @glenn-jocher

  • A restricted-load model test now uses tolerant numerical comparison rather than requiring bit-for-bit identical outputs across all hardware backends.
  • This reduces false failures without weakening validation of model weights, criteria, or fused layers.

• 🛠️ Maintenance and documentation

  • Updated the self-hosted GitHub Actions cleanup runner from v1.4.39 to v1.4.40.
  • Refreshed the embedded video in the Security Alarm System guide.
  • Updated the export documentation to describe the more precise QAT head behavior.

🎯 Purpose & Impact

• More trustworthy dataset updates: Remote NDJSON publishers can change manifests without users accidentally training on outdated data. ✅
• Less unnecessary network and disk usage: Identical datasets are reused efficiently, even when signed image URLs rotate.
• Safer ongoing training: Changed datasets receive separate directories, so existing jobs continue using the files they started with.
• Improved INT8 deployment performance: TensorRT engines should export and run more efficiently while retaining accuracy-sensitive operations in higher precision. ⚡
• Better cross-platform stability: Testing is less likely to fail because of harmless backend-level numerical differences, particularly on embedded and Jetson environments.
• No major user-facing API changes: The release primarily improves conversion reliability, deployment efficiency, and development infrastructure.

What's Changed

• Speed up QAT and PTQ INT8 TensorRT engines by @Y-T-G in #26171
• Compare restricted-load outputs with allclose in test_restricted_load_criterion by @glenn-jocher in #26217
• Bump eviden-actions/clean-self-hosted-runner from v1.4.39 to v1.4.40 in /.github/workflows by @UltralyticsAssistant in #26229
• Add https://youtu.be/ij9cnYM0EVE to docs by @RizwanMunawar in #26235
• Refresh remote NDJSON at the conversion owner by @cainiao33 in #26242

Full Changelog: v8.4.155...v8.4.156