ultralytics 8.4.153

v8.4.153 - Fix SAM3 compile=False handling and release 8.4.153 (#26189)

on Python PyPI

🌟 Summary

Ultralytics 8.4.153 delivers a critical SAM3 initialization fix, improves validation reliability, clarifies INT8 export behavior, and expands Ultralytics Platform documentation and annotation capabilities. 🚀

📊 Key Changes

• 🛠️ Fixed SAM3 compile=False handling — PR #26189 by @glenn-jocher

  • Converts the predictor option correctly before passing it to the SAM3 builder:
    • False disables compilation.
    • True enables the default compilation mode.
    • Explicit compilation mode strings are preserved.
  • Prevents unintended torch.compile activation and the resulting PyTorch 2.14 Dynamo error during SAM3 initialization.
  • Validated across all six Platform SAM models for HTTP and WebSocket inference.
  • Package version bumped to 8.4.153.

• ✅ Improved standalone semantic segmentation validation

  • Polygon-based semantic datasets now receive their background class metadata during dataset construction.
  • Prevents background pixels from being incorrectly assigned to the final real class.
  • Makes standalone validation metrics such as mIoU and pixel accuracy meaningful again.

• 🖼️ Fixed standalone classification validation with extra classes

  • Validation now filters samples whose class IDs are outside the model’s class range, matching existing training behavior.
  • Prevents IndexError crashes when the validation dataset contains more classes than the checkpoint supports.
  • Clear warnings identify skipped samples and mismatched classes.

• 🔄 Preserved explicit datasets when resuming training

  • train(resume=True, data="...") now correctly honors the user-provided dataset instead of silently using the checkpoint’s original dataset.
  • Path-based resumes also avoid injecting an unintended task-default dataset.

• ⚙️ Improved INT8 export behavior and documentation

  • Quantization-aware training checkpoints can export their stored INT8 ranges without calibration data for ONNX and TensorRT.
  • RKNN INT8 validation now correctly applies to detection models only, including INT8-only Rockchip targets.
  • RKNN examples now use a compatible detection model and dataset.
  • TensorRT documentation no longer claims that dynamic is automatically enabled for INT8 exports.

• 🎯 Preserved conf=0.0 during IMX export

  • An explicit zero confidence threshold is no longer replaced with 0.001.
  • Added coverage for both conf=0.0 and the default conf=0.25 behavior.

• 🧩 Expanded Platform annotation and billing documentation

  • Added documentation for dataset-wide batch annotation, class mapping, progress tracking, stopping runs, version snapshots, and billing.
  • YOLO smart annotation now includes pose datasets, while SAM remains available for detection, segmentation, semantic, and OBB tasks.
  • Documented the Platform referrals tab, endpoint uptime charges, batch annotation pricing, supported dataset licenses, and Python 3.11 requirement.
  • Agents documentation now reflects that the feature no longer requires Early Access.

• 📚 Documentation and links updated

  • Repository banners now link to the YOLO27 preview and waitlist.
  • Added Huawei Ascend and Google Coral Edge TPU to the Hailo hardware comparison.
  • Removed the unreachable retired Neptune integration page.

🎯 Purpose & Impact

• More reliable SAM3 deployments: Users on newer PyTorch versions can initialize and run SAM3 without unexpected compilation or Dynamo failures. 🧠
• More trustworthy validation results: Semantic and classification validation now better reflect the actual model and dataset configuration.
• Safer resume workflows: Explicit dataset overrides are respected, reducing the risk of accidentally continuing training on the wrong data.
• Simpler edge deployment: INT8 export rules are clearer and better aligned with actual ONNX, TensorRT, and RKNN behavior.
• More predictable export thresholds: IMX users can intentionally disable filtering with conf=0.0.
• Broader Platform workflows: Pose smart annotation and batch annotation make dataset preparation faster, while updated billing and integration documentation makes Platform usage easier to understand.
• Upgrade recommendation: Users relying on SAM3, standalone validation, training resume, IMX export, or RKNN INT8 export should upgrade to 8.4.153. 🔄

What's Changed

• Update banner destination links to YOLO27 by @raimbekovm in #26177
• Align INT8 export, Platform Python floor and Hailo comparison docs with merged behavior by @raimbekovm in #26174
• Remove the unreachable Neptune integration page by @raimbekovm in #26170
• Fix standalone semantic val mislabeling background pixels as the last class by @cainiao33 in #26176
• Preserve an explicit data= override when resuming training by @cainiao33 in #26175
• Link the YOLO27 waitlist from the coming-soon notice by @raimbekovm in #26188
• Align Platform docs with dataset licenses, the referrals tab and pose smart annotation by @raimbekovm in #26185
• Document Platform batch annotation and its billing by @raimbekovm in #26186
• Fix the RKNN INT8 detect-only gate for INT8-only targets and its docs example by @raimbekovm in #26184
• Preserve conf=0.0 in IMX export score_threshold by @cainiao33 in #26183
• Fix standalone classify val crash on datasets with extra classes by @cainiao33 in #26182
• Fix SAM3 compile=False handling and release 8.4.153 by @glenn-jocher in #26189

Full Changelog: v8.4.152...v8.4.153