Ultralytics YOLO

🌟 Summary

🚀 v8.4.100 expands edge deployment capabilities with Hailo instance segmentation support for YOLOv8 and YOLO11, while improving deployment guidance, training validation, Platform workers, fuzz testing, and export reliability.

📊 Key Changes

• 🟢 Hailo instance segmentation export and inference

  • Added Hailo HEF export support for YOLOv8-seg and YOLO11-seg.
  • Hailo exports raw segmentation tensors, while Ultralytics handles decoding, bounding-box processing, NMS, and mask generation during inference.
  • Segmentation models do not use Hailo's on-chip NMS configuration.
  • YOLO26 segmentation is explicitly rejected with a clear error because Hailo-8/8L hardware cannot allocate its attention-based architecture.

• 📚 Improved Hailo INT8 deployment documentation

  • Added practical accuracy expectations for YOLOv8, YOLO11, and YOLO26 on Hailo-8L.
  • Documents that calibration data should closely match the production domain.
  • Notes that YOLO26 confidence scores may be approximately 0.05 lower on Hailo devices.
  • Explains why attention layers can be more sensitive to INT8 quantization on Hailo-8/8L.

• ⚠️ Clearer TensorRT compatibility guidance

  • Platform documentation now warns that downloaded TensorRT engines must match the target GPU family, TensorRT version, CUDA runtime, and build environment.
  • Users with mismatched environments are directed to export engines locally on the deployment device.

• 🧪 Broader and more effective fuzz testing

  • Fuzzing now covers tracking, cached video, malformed media, Unicode paths, additional model families, and CoreML on macOS.
  • Duplicate commands are skipped and shell-safe reproducer commands are generated.
  • Fuzzing budgets increase from 285 to 300 minutes per run.

• 🛑 Cleaner training errors for invalid small configurations

  • Training now raises an actionable error when batch=1 is used with an image size too small for BatchNorm.
  • This replaces a confusing low-level PyTorch failure with guidance to increase the batch size or image size.

• 🔧 More reliable Axelera exports

  • Pins omnimalloc==0.5.0 to avoid compatibility failures with Axelera Devkit 1.7.0.
  • Runtime checks now validate the same allocator version before export.

• ☁️ Improved Platform managed-worker integration

  • Reuses existing Platform training callbacks for managed workers.
  • Supports configurable Platform API endpoints through PLATFORM_API_URL.
  • Training output paths now respect the current SETTINGS["runs_dir"] value at runtime.

🎯 Purpose & Impact

• Edge users gain a new deployment option: YOLOv8 and YOLO11 segmentation models can now run on Hailo accelerators, including Hailo-8, Hailo-8L, Hailo-10, and Hailo-15.
• Inference behavior remains consistent: Hailo segmentation outputs are converted into the standard Ultralytics format, allowing existing predictor post-processing and mask workflows to remain unchanged.
• Deployment accuracy expectations are clearer: Users can better estimate INT8 performance and avoid misleading comparisons between PyTorch and Hailo results.
• Fewer deployment surprises: TensorRT and Axelera compatibility warnings help users identify environment mismatches before runtime failures.
• More understandable training failures: Invalid batch/image-size combinations now fail early with a practical fix instead of producing an obscure internal exception.
• Higher software reliability: Expanded fuzz coverage and duplicate elimination should uncover more real edge cases while reducing wasted test time.

📌 For the simplest workflow to annotate datasets, train models, and deploy them, visit the Ultralytics Platform.

What's Changed

• Document Hailo INT8 accuracy expectations and deployment guidance by @JESUSROYETH in https://github.com/ultralytics/ultralytics/pull/25260
• Warn on TensorRT/CUDA version mismatch in Platform Models export docs by @lakshanthad in https://github.com/ultralytics/ultralytics/pull/25263
• Expand fuzz exploration envelope by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/25262
• Raise clean error for batch=1 training at minimum imgsz by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/25264
• Fix flaky Axelera export environment by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/25267
• Reuse Platform callbacks for managed workers by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/25266
• Add Hailo segmentation export and inference for YOLOv8 and YOLO11 by @JESUSROYETH in https://github.com/ultralytics/ultralytics/pull/25259

Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.4.99...v8.4.100