ultralytics/ultralytics v8.4.159

v8.4.159 - Skip unused test downloads and report initial training system metrics (#26276)

on GitHub

🌟 Summary

v8.4.159 improves dataset split handling, startup monitoring, and INT8 SavedModel export efficiency.

📊 Key Changes

• 📥 Avoids unnecessary dataset downloads during training

  • NDJSON-to-YOLO conversion now respects the requested training validation split.
  • By default, training with split=val skips unused test images, reducing download time, storage, and preparation overhead.
  • Using split=test retains test images and makes the trainer validate against that selected split.
  • Split-aware caching prevents validation and test conversions from incorrectly reusing the same cached dataset.
  • Behavior remains compatible with standalone conversion, metadata inference, classification datasets without validation data, and depth-calibration provenance.
  • YOLO, RT-DETR, and classification training now consistently use the selected validation split and corresponding dataset fractions.

• 📊 Reports system metrics as soon as training starts

  • The existing training_started Platform request now includes an initial system snapshot.
  • CPU, memory, disk, and related monitoring data can appear before the first epoch completes.
  • Epoch-level reporting remains unchanged, with no additional requests, timers, or periodic monitoring signals.
  • Optional system monitoring remains isolated so it does not become a required training dependency.

• 🧠 Reduces peak memory during INT8 SavedModel export

  • Calibration data is now created only when needed for the ONNX-to-SavedModel conversion and released immediately afterward.
  • Testing showed an approximately 533.7 MiB, or 18.2%, reduction in peak process memory on an Apple M4 system.
  • Export behavior, runtime, calibration data, and tested full-integer TFLite outputs remain unchanged.

• 🧪 Adds and updates tests and documentation

  • Tests now cover validation/test split selection, cache behavior, and depth calibration handling.
  • The Platform callback documentation now includes the internal system-metrics helper.

🎯 Purpose & Impact

• ⚡ Faster, lighter dataset preparation: Training workflows using Platform NDJSON datasets should avoid downloading images that are not needed for the selected split.
• ✅ More predictable validation: The split setting now consistently controls which data is prepared, loaded, and evaluated across supported tasks.
• 👀 Better training visibility: Platform users can see system resource information immediately when a run begins instead of waiting for the first epoch.
• 💻 More reliable exports on memory-constrained devices: INT8 SavedModel exports require less peak memory, reducing the risk of out-of-memory failures while preserving existing export results.
• 🔄 Minimal workflow disruption: Existing epoch monitoring, standalone conversion, classification fallbacks, and export behavior are preserved.

What's Changed

• Reduce peak memory during INT8 SavedModel export by @amanharshx in #26272
• Skip unused test downloads and report initial training system metrics by @glenn-jocher in #26276

Full Changelog: v8.4.158...v8.4.159