ultralytics 8.4.101

v8.4.101 - Add Hailo pose, OBB, and classification export and inference for YOLOv8 and YOLO11 (#25276)

on Python PyPI

🌟 Summary

🚀 Ultralytics v8.4.101 expands Hailo deployment support across nearly the full YOLO task range and strengthens NDJSON dataset reliability, Platform validation insights, and documentation.

📊 Key Changes

• 🧠 Expanded Hailo export and inference

  • format="hailo" now supports:
    • Object detection
    • Instance segmentation
    • Pose estimation
    • Oriented bounding boxes (OBB)
    • Image classification
  • Added support for YOLOv8 and YOLO11 pose, OBB, and classification models.
  • Added YOLO26 classification export support.
  • Hailo classification models now return probabilities directly using on-chip softmax.
  • Pose and OBB outputs are decoded through the standard Ultralytics inference pipeline.
  • YOLO26 pose, OBB, and segmentation remain unsupported and are rejected before compilation.
  • Pose, OBB, and classification paths were validated on Hailo-8L with HailoRT 4.23 and DFC 3.33. Other targets are accepted but require separate validation.

• 🛡️ More reliable NDJSON dataset conversion

  • Signed NDJSON URLs with authentication query parameters are now recognized correctly.
  • Added file locking to prevent multiple training jobs from converting the same dataset simultaneously.
  • Interrupted downloads can resume without unnecessarily redownloading completed images.
  • Dataset completion markers prevent partially converted datasets from being reused.
  • Preserves safe content hashes in generated filenames while maintaining collision protection.
  • Added filelock as a required dependency.

• 📈 Improved Platform validation reporting

  • Training now records both the worst-performing and best-performing validation images.
  • Reports up to 25,000 images from each cohort, while preserving the existing 50,000-row transport limit.
  • Includes true positives, false positives, and false negatives for each selected image.

• 🏢 Clearer On Premise data boundaries

  • Documentation now explains which data stays local and which information is uploaded.
  • Source dataset pixels remain on the local machine, while labels, annotations, metrics, and the best model checkpoint can be sent to Platform.
  • Documents the exact Docker images used for ARM64, CPU, and NVIDIA environments.

• 🗂️ Improved dataset ingest documentation

  • Documents the classMapping parameter for mapping incoming archive classes to existing dataset classes.
  • Supports numeric class indexes, class names, and null to skip labels.

• 📚 Documentation and maintenance updates

  • Refreshed the VisDrone tutorial video and MNIST creator link.
  • Removed the obsolete HUB-SDK entry from the CI documentation.
  • Added internal NDJSON conversion reference documentation.

🎯 Purpose & Impact

• 🚀 Broader edge deployment options: Hailo users can deploy more YOLO task types directly to Hailo HEF without building separate conversion pipelines.
• ⚡ Better hardware acceleration: Classification softmax runs on the Hailo device, while pose and OBB decoding integrates with normal Ultralytics inference.
• 🔒 Safer concurrent training: Multiple jobs using the same NDJSON source are less likely to corrupt or duplicate cached data.
• ⏱️ Faster recovery from interruptions: Failed image downloads can resume from the existing cache instead of starting over.
• 🔍 More actionable model evaluation: Platform users can inspect both failure cases and strong-performing validation examples, making dataset and model improvements easier.
• 🏢 More transparent enterprise workflows: On Premise users have clearer expectations about data residency and model-weight uploads.
• ⚠️ Deployment note: Hailo compilation is hardware- and compiler-specific. New pose, OBB, and classification exports should be validated on the target accelerator before production use.

What's Changed

• Add https://youtu.be/rTblZN9IRDo to docs by @RizwanMunawar in #25268
• docs: document classMapping parameter for dataset ingest by @amanharshx in #25272
• Fix MNIST creator link and CI table by @glenn-jocher in #25273
• Document On Premise training data boundaries by @glenn-jocher in #25274
• Fix training from signed NDJSON URLs by @glenn-jocher in #25275
• Add Platform image validation metrics by @glenn-jocher in #25265
• Capture best and worst validation images by @glenn-jocher in #25279
• Fix concurrent NDJSON cache reuse by @glenn-jocher in #25278
• Add Hailo pose, OBB, and classification export and inference for YOLOv8 and YOLO11 by @JESUSROYETH in #25276

Full Changelog: v8.4.100...v8.4.101