ultralytics 8.4.89

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

on Python PyPI

🌟 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: v8.4.88...v8.4.89