github ultralytics/ultralytics v8.4.103
v8.4.103 - Prevent training from ending during warmup (#25321)
on GitHub

latest release: v8.4.104
20 hours ago

🌟 Summary

v8.4.103 improves training reliability, result handling, deployment workflows, and documentation—most importantly ensuring warmup finishes on schedule so short training runs reach their intended learning-rate behavior. 🚀

📊 Key Changes

  • Training warmup no longer consumes entire short runs 🎯

    • warmup_epochs is now treated as a true epoch count rather than being forced to at least 100 iterations.
    • Warmup is capped so at least the final planned epoch uses the regular learning-rate schedule.
    • Warmup calculations are centralized and remain consistent after batch-size changes, out-of-memory recovery, and timed-training adjustments.
    • Example: a 10-epoch run with 3 warmup epochs now warms up for 3 epochs instead of potentially staying in warmup for the entire run.

  • More accurate validation and experiment reporting 📈

    • Confusion matrices now honor the confidence threshold supplied by the validator instead of silently overriding it to 0.25.
    • Validation samples sent to the Platform now include evenly ranked F1 samples across the full image distribution, plus separate best and worst cohorts.
    • Segmentation, pose, and raw confusion-matrix plots are no longer dropped from experiment trackers such as W&B, ClearML, Neptune, and DVC.

  • Improved model construction and performance ⚡

    • Fixed backbone width scaling when a layer’s channel count happens to equal the number of classes, preventing unexpectedly oversized models.
    • Accelerated ModelEMA updates with batched PyTorch operations where supported.
    • Made model profiling safer by profiling copied layers rather than modifying shared model modules.

  • More robust results and mask processing 🖼️

    • Results methods such as plot(), save_txt(), save_crop(), summary(), and verbose() now work correctly with NumPy-backed results from Results.numpy().
    • Mask processing now binarizes masks before cropping while preserving output behavior.
    • Image-list prediction sources now follow HTTP 308 redirects on supported Python versions.

  • Safer and clearer exports 🔧

    • CoreML compatibility checks now run before conversion and reject unsupported dynamic classification and RT-DETR exports earlier.
    • Removed the unsupported Hailo opset argument.
    • RKNN exports now warn users when a requested opset above 19 is automatically reduced.

  • Expanded Platform and API capabilities ☁️

    • Platform REST API documentation now covers supported API-key operations across datasets, models, training, exports, deployments, storage, activity, and workspaces.
    • Added documentation for Slack alerts for training, export, and deployment results.
    • Updated Platform On Premise and Docker image references to use rolling latest tags.
    • Linux GPU Docker guidance now recommends CDI --device reservations for more reliable access across host daemon reloads.

  • Documentation and workflow improvements 📚

    • YOLO26 documentation now presents inference quickstarts above the fold and points open-vocabulary YOLOE-26 content to its dedicated page.
    • Guides are organized by project stage, from data preparation through deployment and edge hardware.
    • Added contextual “What’s Next” links across prediction, dataset, training, validation, and export pages.
    • Updated broken Tiger-Pose video links and moved YOLOv5 documentation into a clearly labeled legacy section.
    • Updated GitHub Actions workflows to use actions/setup-python@v7.

🎯 Purpose & Impact

  • Short training jobs now train as intended ✅ Users no longer risk spending an entire run in warmup with very low learning rates, which should improve optimization and make results more representative of the configured schedule.
  • Metrics and diagnostics are more trustworthy 📊 Confidence thresholds, confusion matrices, F1 samples, and tracker plots now better reflect actual validation settings and model behavior.
  • Model sizes are more predictable 🧠 Models created from YAML configurations will no longer become unexpectedly large in certain class-count configurations.
  • NumPy interoperability is substantially better 🔄 Users can convert results to NumPy and continue using common output, plotting, cropping, and export methods.
  • Deployment is safer and easier to troubleshoot 🛠️ Unsupported export combinations fail earlier, while RKNN overrides and Docker GPU requirements are now communicated clearly.
  • Platform users get better automation and integration options 🔔 Slack alerts and broader API-key support make it easier to monitor and manage training and deployment programmatically.
  • Recommended model: For new projects, use YOLO26, or streamline annotation, training, and deployment with Ultralytics Platform.

What's Changed

Full Changelog: v8.4.102...v8.4.103

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