fix(deps): update dependency ultralytics ( 8.4.87 → 8.4.89 ) - #442

v8.4.89: - Fix 2D grayscale NumPy array prediction on color models (#​24751)

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🌟 Summary

Ultralytics v8.4.89 improves prediction reliability for grayscale NumPy images, adds several important edge-case fixes, and makes training, benchmarking, AI Gym, and release publishing more robust 🚀

📊 Key Changes

• 🖼️ Fixed 2D grayscale NumPy prediction on color models by @​maxime2476

  • Raw grayscale NumPy arrays shaped like (H, W) are now expanded correctly for 3-channel color models.
  • This makes NumPy inputs behave consistently with PIL images and file paths.
  • Prevents PyTorch channel mismatch crashes during prediction.

• 🏋️ Fixed AI Gym result alignment by @​SuperMarioYL

  • workout_count, workout_stage, and workout_angle now only report currently visible tracked people.
  • Results now stay aligned with total_tracks, avoiding stale data from people who already left the frame.

• ⚡ Improved multi-GPU training performance by @​ExtReMLapin

  • Distributed training now uses broadcast_buffers=False by default.
  • This can reduce unnecessary GPU synchronization overhead, especially on non-NVLink systems or high-resolution training.

• 🧪 Improved benchmark usability by @​zhanghuiwan and @​raimbekovm

  • Benchmark format values are now case-insensitive, so inputs like ONNX or TensorRT work as expected.
  • Benchmark docs now include the eps argument and clarify standalone benchmark() defaults, including model="yolo26n.pt" and imgsz=160.

• 🌍 Fixed YOLOE and YOLO-World CLI class parsing by @​ahmet-f-gumustas

  • Class names passed through the CLI now strip extra whitespace.
  • For example, classes="person, bus" now becomes ["person", "bus"] instead of ["person", " bus"].

• 🔗 Fixed signed model URL suffix validation by @​diaz3z

  • URLs like model.pt?token=abc now pass .pt suffix checks correctly.
  • This improves compatibility with private or authenticated model downloads.

• 📐 Fixed segment2box() for objects on the left image edge by @​bujna94

  • Segments where all x-coordinates are 0 are no longer incorrectly dropped.
  • This improves bounding box generation for objects touching the image border.

• 🧩 Improved installation reliability by @​Nailujj

  • Excludes the known problematic opencv-python==4.13.0.90 package.
  • Helps avoid OpenCV-related crashes in environments such as Databricks.

• 🛠️ Hardened release publishing workflow by @​glenn-jocher

  • Manual recovery runs are now safer after partial release failures.
  • Existing PyPI artifacts and release uploads are handled more gracefully.
  • Slack notifications now wait for publishing and SBOM generation to finish.

🎯 Purpose & Impact

• ✅ More reliable inference inputs
  Users can now pass grayscale NumPy arrays directly to standard color YOLO models without manual channel conversion.

• 🔄 More consistent behavior across input types
  NumPy, PIL, and file-based grayscale images are now normalized more consistently before prediction.

• 🚀 Better distributed training efficiency
  Multi-GPU users may see reduced synchronization bottlenecks, especially in bandwidth-limited setups.

• 📊 Clearer and more forgiving benchmarking
  Benchmark workflows are easier to use, with fewer avoidable errors from capitalization or undocumented defaults.

• 🏋️ Cleaner AI Gym analytics
  Workout tracking outputs are easier to consume because per-person lists now match the people currently visible in the frame.

• 🔐 Better support for real-world deployment workflows
  Signed URLs and managed Python environments are handled more reliably, reducing friction for cloud and enterprise users.

• 🧪 Stronger regression coverage
  New tests protect the grayscale NumPy fix, signed URL handling, segment2box() border behavior, and CLI class parsing from future regressions.

What's Changed

• Harden publish workflow recovery, notifications, and dispatch guard by @​glenn-jocher in #​25027
• Add GhostNetV2 (DFC attention) modules and YOLOv8 GhostV2 configs by @​kaanrkaraman in #​23108
• Update README by @​versa-dev in #​23656
• Strip whitespace from CLI classes for YOLOE/World by @​ahmet-f-gumustas in #​24677
• Exclude broken opencv-python 4.13.0.90 wheel by @​Nailujj in #​23376
• Fix AIGym workout result lists to align with visible track IDs by @​SuperMarioYL in #​24228
• Strip URL query strings in check_suffix for signed model URLs by @​diaz3z in #​24369
• Set DDP broadcast_buffers=False by default by @​ExtReMLapin in #​24412
• Make benchmark format comparison case-insensitive by @​zhanghuiwan in #​24453
• Document benchmark eps arg and standalone benchmark() defaults by @​raimbekovm in #​24560
• Fix segment2box dropping segments on the left image edge by @​bujna94 in #​24679
• Fix 2D grayscale NumPy array prediction on color models by @​maxime2476 in #​24751

New Contributors

• @​SuperMarioYL made their first contribution in #​24228
• @​Nailujj made their first contribution in #​23376
• @​maxime2476 made their first contribution in #​24751
• @​versa-dev made their first contribution in #​23656
• @​diaz3z made their first contribution in #​24369
• @​zhanghuiwan made their first contribution in #​24453
• @​bujna94 made their first contribution in #​24679

Full Changelog: ultralytics/ultralytics@v8.4.88...v8.4.89

v8.4.88: - Cap dataloader workers for tiny datasets (#​25031)

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🌟 Summary

Ultralytics v8.4.88 focuses on more reliable training for tiny datasets, stable NCNN export, and clearer documentation for model training concepts. 🚀

📊 Key Changes

• Tiny dataset dataloader fix by @​glenn-jocher 🧠

  • Dataloader worker processes are now capped based on the actual number of final batches.
  • Single-batch training loaders now run in-process instead of spawning extra persistent workers.
  • Empty dataset handling now correctly relies on PyTorch DataLoader validation instead of triggering internal worker-count errors.
  • Added regression tests for tiny loaders, distributed sampling with drop_last, and empty dataset behavior.

• Improved NCNN export stability by @​glenn-jocher 🛠️

  • PNNX is pinned to 20260526 for NCNN export and CI checks.
  • This avoids a known newer PNNX regression that could cause NCNN inference crashes.
  • NCNN remains paired with the latest available ncnn wheel.

• Clearer training documentation for dfl by @​fcakyon 📚

  • The dfl training argument description now correctly explains Distribution Focal Loss as a bounding box localization term, not a classification feature.
  • This helps users better understand how YOLO models learn accurate box positions.

• Knowledge distillation guide visual update by @​RizwanMunawar 🖼️

  • Added a new workflow image to the knowledge distillation documentation.
  • The image helps explain the teacher-student model training concept more intuitively.

• Version bump 📦

  • Package version updated from 8.4.87 to 8.4.88.

🎯 Purpose & Impact

• Better experience for small experiments and quick tests ⚡
  Users training on very small datasets should see fewer unnecessary background worker processes, lower overhead, and fewer cases where stuck workers keep CUDA resources busy.

• More robust distributed and edge-case training behavior ✅
  The dataloader update preserves existing drop_last behavior while improving worker handling after sampler construction, making training safer across normal, tiny, and distributed dataset setups.

• More dependable NCNN deployment workflows 📱
  Pinning PNNX improves reliability for users exporting YOLO models, including YOLO26 models, to NCNN for lightweight or edge-device inference.

• Clearer learning resources for all users 🌐
  Documentation updates make advanced concepts like knowledge distillation and Distribution Focal Loss easier to understand for both new users and experienced developers.

What's Changed

• Add cover image for knowledge-distillation.md by @​RizwanMunawar in #​25026
• Pin PNNX for NCNN export stability by @​glenn-jocher in #​25028
• Clarify DFL argument describes box localization not classification by @​fcakyon in #​25029
• Cap dataloader workers for tiny datasets by @​glenn-jocher in #​25031

Full Changelog: ultralytics/ultralytics@v8.4.87...v8.4.88