fix(deps): update dependency ultralytics ( 8.4.92 → 8.4.93 ) - #452

v8.4.93: - Enable dynamic shapes and NMS for Alibaba MNN export (#​22348)

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🌟 Summary

Ultralytics v8.4.93 expands Alibaba MNN export capabilities, improves training and inference reliability, and adds major dataset, cloud-storage, and integration documentation updates. 🚀

📊 Key Changes

• Alibaba MNN exports now support dynamic shapes and embedded NMS 🎯

  • dynamic=True enables variable image dimensions for MNN models.
  • nms=True is supported for detect and pose models with static shapes.
  • Unsupported combinations now fail clearly:
    • Dynamic shapes with NMS
    • NMS for segmentation and OBB models
  • Empty NMS outputs are handled safely only when the exported metadata confirms NMS is enabled.
  • Real MNN 3.5.0 inference was validated with YOLO11 detection, pose, and OBB workflows.

• Improved model tracking and vision workflows 👁️

  • Fixed native ReID crashes in BoT-SORT when users provide detections directly without feature tensors.
  • Polygon-based ObjectCounter now determines IN/OUT direction from object motion rather than region shape, improving counting in square and wide regions.
  • YOLOE visual prompts now work with PyTorch tensor inputs.
  • embed=[] no longer causes prediction to fail.

• More reliable training, benchmarking, and export behavior 🛠️

  • Semantic segmentation loss remains finite when an entire batch contains ignore pixels.
  • Legacy benchmark(half=True) and benchmark(int8=True) flags now correctly map to quantized benchmarking instead of silently using FP32.
  • Invalid configuration values such as nbs=0, max_det=0, mask_ratio=0, or negative seeds are rejected early with clear errors.
  • TorchScript optimization now reports a helpful message when XNNPACK is unavailable.
  • CPU torch.compile gracefully falls back to eager execution when no C++ compiler is available.
  • CoreML exports use a safer NumPy compatibility limit to avoid NumPy 2.4 crashes.
  • Axelera exports reject image sizes below the backend’s 64-pixel minimum.

• Improved segmentation and conversion utilities ⚡

  • segment2box is significantly faster while preserving its output.
  • process_mask(upsample=True) now upsamples before cropping, preventing mask pixels from leaking outside bounding boxes.
  • Segment-mask conversion now creates missing output directories and handles Windows grayscale image shapes correctly.
  • Deprecated crop_fraction usage now emits a warning instead of stopping classification transforms.

• Expanded pose evaluation customization 🧍

  • Pose datasets can define custom kpt_oks_sigmas values in their YAML files.
  • Values are validated against the configured number of keypoints and must be positive.

• Ultralytics Platform cloud-storage integrations documented ☁️

  • Added guides for connecting Google Cloud Storage, Amazon S3, and Azure Blob Storage.
  • Platform can index images and YOLO labels in place without copying the source data.
  • Documentation covers read-only credentials, dataset creation, revision pinning, import limits, retries, and current limitations.
  • These integrations require a Pro or Enterprise plan.

• Documentation and deployment updates 📚

  • Added documentation for Apple Core AI and its future .aimodel workflow. Core AI export is not yet available; Core ML remains the supported Apple deployment path.
  • Dataset guides now document using .txt image lists for train, validation, and test splits.
  • Segmentation dataset documentation was refreshed with corrected split counts, class information, download sizes, and clearer usage guidance.
  • Rust inference examples now use ultralytics-inference 0.0.27.
  • Python Docker images now use Python 3.13 on Debian Trixie.
  • Environment variables accept common truthy values such as 1, yes, on, y, and t.

🎯 Purpose & Impact

• Broader deployment support: MNN users can target applications that need variable input sizes or built-in post-processing, while clear restrictions prevent unsupported exports from failing later.
• Fewer runtime crashes: Tracking, segmentation training, tensor-based YOLOE inference, compilation, and export workflows now handle common edge cases more safely.
• More trustworthy results: Fixes to mask cropping, object-counting direction, semantic loss, and benchmark precision ensure outputs and metrics better reflect user settings.
• Faster development cycles: The optimized segment2box implementation and improved small-dataset documentation make testing and experimentation more efficient.
• Simpler cloud workflows: Platform users can train on existing cloud datasets without manually downloading and re-uploading large collections.
• Clearer configuration errors: Invalid arguments are detected at the configuration layer with actionable messages instead of obscure deep runtime tracebacks.
• Apple deployment guidance is clearer: Users can continue using supported Core ML exports today while understanding that Core AI support is planned for a future release. 🚀

What's Changed

• Bump slackapi/slack-github-action from v3.0.3 to v3.0.5 in /.github/workflows by @​UltralyticsAssistant in #​25110
• Add Platform cloud storage integration docs (GCS, S3, Azure) by @​glenn-jocher in #​25112
• Split Platform cloud storage docs into per-vendor pages by @​glenn-jocher in #​25113
• Fix ObjectCounter polygon direction to follow object motion by @​JESUSROYETH in #​25104
• Fix BOTSORT native-ReID crash with user-supplied detections by @​JESUSROYETH in #​25102
• Pin numpy<=2.3.5 for CoreML export to avoid numpy>=2.4 crash by @​fcakyon in #​25100
• Fix silent FP32 fallback for legacy precision flags in benchmark by @​JESUSROYETH in #​25106
• Speed up segment2box by @​Y-T-G in #​25095
• Fix NaN semantic loss when the whole batch is ignore pixels by @​JESUSROYETH in #​25097
• Validate bool-or-str CLI args (compile) at cfg layer (#​25056) by @​Joe-KI333 in #​25082
• Refactor segmentation dataset documentation by @​raimbekovm in #​25063
• Validate integer minimums and compile type at cfg layer by @​glenn-jocher in #​25114
• Fail cleanly for optimize=True on PyTorch builds without XNNPACK by @​glenn-jocher in #​25116
• Fall back to eager when torch.compile fails at first forward by @​glenn-jocher in #​25115
• Fix ValueError when predict is called with an empty embed list by @​ahmet-f-gumustas in #​25123
• Fix segment mask conversion output directory by @​zhanghuiwan in #​25117
• Upgrade Python Docker image to 3.13 Trixie by @​glenn-jocher in #​25127
• Add Apple Core AI integration guide by @​glenn-jocher in #​25129
• Fix raise DeprecationWarning halting classify_transforms by @​Dheeraj-Bhaskaruni in #​24236
• Update ultralytics-inference version to 0.0.27 in documentation by @​onuralpszr in #​25130
• Preserve save_dir override when resuming training by @​yegeniy in #​23623
• Upsample before crop in process_mask to prevent mask leakage by @​wjddnwp29 in #​24463
• Accept common truthy strings for boolean environment variables by @​raimbekovm in #​24730
• Document .txt image-list usage in dataset YAML splits by @​raimbekovm in #​24472
• Reject undersized Axelera exports by @​glenn-jocher in #​25131
• Fix YOLOE tensor input support with visual prompts by @​emmanuel-ferdman in #​22251
• Support optional kpt_oks_sigmas for pose evaluation via dataset YAML by @​cosmo-gb in #​24381
• Enable dynamic shapes and NMS for Alibaba MNN export by @​Y-T-G in #​22348

New Contributors

• @​Dheeraj-Bhaskaruni made their first contribution in #​24236
• @​yegeniy made their first contribution in #​23623
• @​Joe-KI333 made their first contribution in #​25082
• @​wjddnwp29 made their first contribution in #​24463
• @​cosmo-gb made their first contribution in #​24381

Full Changelog: ultralytics/ultralytics@v8.4.92...v8.4.93