ultralytics 8.4.40

v8.4.40 - `ultralytics 8.4.40` Per-image Precision and Recall (#24089)

on Python PyPI

🌟 Summary

Ultralytics v8.4.40 introduces per-image precision/recall/F1 tracking during validation (led by PR #24089 from @Laughing-q), making it much easier to see exactly which images your model handles well or poorly. 📈🖼️

📊 Key Changes

• New per-image validation metrics added to results:
  • precision, recall, f1, tp, fp, fn for each image.
  • Exposed via metrics.box.image_metrics (and also for seg and pose where applicable). ✅
• Detection validation pipeline updated to store image name and compute image-level stats consistently with validation matching logic. 🔍
• Distributed (multi-GPU) validation support now gathers and merges image_metrics correctly across ranks, so results remain complete in larger training setups. 🧠⚙️
• Metrics classes extended with:
  • image_metrics storage
  • update helpers
  • clear/reset helpers to prevent stale metrics between runs.
• Docs updated across validation/task guides (detect, segment, pose, OBB, insights, custom trainer) with examples showing how to access per-image metrics. 📚
• Version bump:
  • 8.4.39 ➜ 8.4.40 🚀

🎯 Purpose & Impact

• Faster debugging of weak samples: You can now pinpoint problematic images directly instead of relying only on dataset-wide averages. 🎯
• Better dataset curation: Find images causing high false positives/false negatives and decide whether to relabel, augment, or rebalance. 🧹
• More actionable model evaluation: Teams get practical, image-level insight for error analysis and iterative improvement. 🔁
• Reliable at scale: Works cleanly in multi-GPU validation, so enterprise and research workflows benefit too. 🏗️
• Broad usability: Useful for both beginners and advanced users working with YOLO models, especially YOLO26 validation workflows. 🤝

What's Changed

• ultralytics 8.4.40 Per-image Precision and Recall by @Laughing-q in #24089

Full Changelog: v8.4.39...v8.4.40