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v8.4.104 - New YOLO26-Depth monocular depth estimation task (#25065)

🌟 Summary

YOLO26 gains a complete monocular depth estimation task, enabling per-pixel distance prediction, training, validation, visualization, calibration, and deployment alongside existing Ultralytics tasks. 🌍📐

📊 Key Changes

• New YOLO26-Depth model family 🤖

  • Adds yolo26n-depth, yolo26s-depth, yolo26m-depth, yolo26l-depth, and yolo26x-depth.
  • Uses a DPT-style depth head that fuses multi-scale YOLO26 features to produce dense depth maps aligned with the input image.
  • Supports both unbounded log-depth output for short- and long-range scenes and bounded depth output modes.

• Depth is now a first-class Ultralytics task 🆕

  • Available through the CLI:

    yolo depth train
    yolo depth val
    yolo depth predict
    yolo export

  • Fully supported through the Python Model API, including prediction, training, validation, export, and model.calibrate().

• Improved depth training and evaluation 📊

  • Adds depth-specific loss functions combining scale-aware depth accuracy with multi-scale edge and gradient matching.
  • Adds standard depth metrics such as delta1, delta2, delta3, absolute relative error, RMSE, and SILog.
  • Handles sparse or invalid ground-truth pixels safely.

• Depth-aware data pipeline 🗂️

  • Introduces paired RGB image and .npy float32 depth-map loading.
  • Applies geometric transformations and flips consistently to images and depth maps.
  • Adds validation for missing, corrupt, incorrectly shaped, or unreadable depth targets.
  • Adds Depth8, an 8-image dataset for rapid pipeline testing and debugging.

• Large dataset and benchmark support 🌐

  • Adds configurations and documentation for NYU Depth V2, KITTI, SUN RGB-D, ARKitScenes, DIODE, Hypersim, TartanAir, Virtual KITTI 2, and others.
  • Documents zero-shot evaluation on NYU Depth V2, KITTI, ETH3D, Make3D, and iBims-1.
  • Released models are pretrained on a broad mix of approximately 2.19 million indoor, outdoor, synthetic, real-world, and pseudo-labeled images.

• Depth visualization and results support 🎨

  • Adds DepthMap results, depth heatmap plotting, depth-aware result summaries, and access through result.depth.data.
  • Depth predictions can be converted through the existing .cpu() and .numpy() workflows.

• Export and deployment support 🚀

  • Adds depth export support for formats such as ONNX and TensorRT.
  • Dynamic exported output shapes track the input image dimensions.
  • TensorRT documentation now correctly lists the supported opset and workspace arguments.
  • Explicitly prevents unsupported NMS or IMX export configurations for depth models.

• More reliable training logs 🧾

  • Loss components are now returned as name-keyed dictionaries rather than positional tensors.

... (truncated)

• a62cbdf New YOLO26-Depth monocular depth estimation task (#25065)
• 04e29c7 Make threaded plot threads non-daemon so interpreter shutdown joins them (#25...
• 6bf16f5 Fix safe_download tar extraction crash with delete=True (#25340)
• 74e6346 update ultralytics-inference version to 0.0.30 in documentation (#25339)
• 514600f Cache semantic mask bit-depth to avoid reopening every epoch (#24985)
• 564ba48 Return loss items as name-keyed dicts from criteria (#25330)
• 8c39458 fix: list TensorRT export arguments (#25338)
• 686c16a Add Objects365 pretrained YOLO26 models to dataset docs (#25333)
• 7460c93 Batch TaskAlignedAssigner select_topk_candidates into a single scatter_add_ (...
• 6b8636e Compute DFL log_softmax once instead of two cross_entropy calls (#25329)
• Additional commits viewable in compare view

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