build(deps): update ultralytics requirement from >=8.4.115 to >=8.4.138 in /apps/ai

[dependabot]

Updates the requirements on ultralytics to permit the latest version.

Release notes

Sourced from ultralytics's releases.

v8.4.138 - Fix legacy checkpoint and YOLO-World loading (#26032)

🌟 Summary

Ultralytics v8.4.138 is a stability-focused release that restores compatibility with older checkpoints, fixes YOLO-World loading, and improves training, inference, tracking, tuning, and documentation reliability. 🛠️

📊 Key Changes

• Legacy checkpoint loading fixed 🎯
  Restricted checkpoint loading now recognizes loss and assignment classes stored in checkpoints created before 8.4.95, including detection, classification, pose, segmentation, rotated-box, and keypoint-related components.

• YOLO-World loading fixed 🌍
  Corrects package loading issues affecting YOLO-World models and related checkpoints.

• MuSGD with channels_last no longer crashes ⚡
  Replaces an incompatible tensor flattening operation with a layout-safe one, allowing CUDA training with the MuSGD optimizer and automatic channels_last support to run correctly.

• SAM feature extraction optimized 🧠
  SAM, SAM2, and SAM3 image embeddings are computed in inference mode, preventing unnecessary autograd graphs from being retained and reducing memory overhead during repeated inference.

• Distributed training made more robust 🔧

  • Prevents DDP failures when a mini-batch contains no assigned targets.
  • Ensures detection, pose, OBB, and related model branches remain connected to the training graph even when there are no positive samples.

• Tuning results corrected 📈
  Multi-dataset tuning now preserves dataset names and iteration order across distributed workers. Failed datasets are recorded with zero metrics instead of incomplete results, keeping tuning histories and fitness plots consistent.

• Classification inference preprocessing improved 🚀
  More preprocessing work is moved to the inference device and performed in batches, which can reduce CPU overhead and improve classification throughput.

• BoT-SORT tracking made faster 🏃
  Global motion compensation now caps corner detection at 400 points instead of 1,000, reducing optical-flow computation while retaining sufficient information for motion estimation.

• Precision and quantization documentation clarified 📚
  Documentation now explains that quantize may select or request different runtime precisions depending on the export format. This avoids implying that every backend supports the same FP16, FP32, or INT8 behavior.

• Documentation quality updates ✨
  Markdown tables were consistently formatted, the OBB navigation label was cleaned up, the TrackZone video was updated, and the YOLO26 CPU speed comparison now clearly identifies its YOLO26n-versus-YOLO11n ONNX baseline and hardware.

🎯 Purpose & Impact

• More users can load existing models without retraining, especially those using checkpoints created with older Ultralytics versions. ✅
• YOLO-World workflows become more dependable for users loading supported models and checkpoints.
• Platform and cloud GPU training jobs are less likely to fail, particularly MuSGD runs using CUDA and channels_last.
• Distributed training and hyperparameter tuning produce more trustworthy results, even with empty-target batches or failed datasets.
• SAM inference can use less memory, which is helpful for long-running applications and repeated image embedding extraction.
• Tracking pipelines may run with lower latency, especially in applications where BoT-SORT global motion compensation is a significant cost.
• Users should review the updated quantization guidance before assuming that quantize=16 or quantize=32 produces the same computation precision across PyTorch, ONNX, OpenVINO, NCNN, MNN, Triton, and other backends. 🔍

What's Changed

• Optimize SAM image feature extraction by @​glenn-jocher in ultralytics/ultralytics#26011

... (truncated)

Commits

• dad7bb4 Fix legacy checkpoint and YOLO-World loading (#26032)
• a685b3e Remove redundant regression scaffolding (#26029)
• c50d476 Fix tuner metrics for failed datasets (#26028)
• 0dc3d1a Move classification predictor preprocessing to the inference device (#26023)
• 3f42bf7 Document what precision quantize selects for each format (#26022)
• c1cb1b7 Fix DDP unused-gradient crash when a mini-batch has no assigned targets (#26012)
• 957b039 Fix MuSGD crash with channels_last: use reshape for non-contiguous grads (#26...
• 18e1129 Halve BoT-SORT global motion compensation cost by capping corner detection at...
• e6754ce Name the YOLO26n vs YOLO11n baseline for the 43% CPU figure (#26025)
• 89367be Add https://youtu.be/Zikt3a7PngE to docs (#26005)
• Additional commits viewable in compare view

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