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v8.4.113 - Add Dragonwing IQ-8275 QNN export target (#25558) source code.tar.gz 2026-07-30 2.6 MB
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🌟 Summary

🚀 Ultralytics 8.4.113 expands Qualcomm QNN deployment to Dragonwing IQ-8275 devices, improves model visualization and export reliability, and standardizes task support documentation across the project.

📊 Key Changes

  • Qualcomm Dragonwing IQ-8275 QNN export
  • Adds iq-8275 and qcs8275 as QNN export targets using Qualcomm SoC model 82.
  • Centralizes the mapping between supported QNN targets and their HTP or SoC provider options.
  • Adds documentation for supported Snapdragon and Dragonwing targets, including the unsupported IQ-615.
  • Enables QNN export testing on Linux x86-64 with onnxruntime-qnn==2.4.0.
  • Example usage: model.export(format="qnn", name="iq-8275").

  • Requires a compatible Qualcomm/Yocto BSP and target-side QNN, FastRPC, DSP firmware, and driver components.

  • New class activation heatmaps for prediction 🔥

  • Replaces the previous visualize=True feature-map dump with a more useful LayerCAM-style heatmap.
  • Saves one heatmap image per input, showing which image regions influenced the predicted class scores.
  • Respects confidence and class filters and is available for PyTorch models.

  • Reduces output clutter compared with the former multi-file feature-map visualization.

  • More accurate attention FLOPs reporting 📊

  • THOP-based profiling now counts functional attention matrix multiplications in YOLO12 attention blocks.
  • Models containing area attention are measured at the requested image size rather than extrapolated from a small stride-sized input.

  • Reported GFLOPs should better reflect the real computational cost of attention-heavy models.

  • Improved RKNN and quantized export support

  • Normalizes detection and pose coordinates during INT8 RKNN export to preserve class-score precision.
  • Restores coordinates at runtime for RKNN inference.
  • Adds compatibility handling for current rknn-toolkit2 dependencies, including setuptools<82.

  • Improves ONNX compatibility and cleans up temporary normalized graphs after export.

  • Export and runtime reliability fixes 🛠️

  • Resets detection shape caches at TorchScript, ONNX, and OpenVINO export boundaries.
  • Avoids redundant TorchScript retracing.
  • Replaces ONNX advanced indexing with gather where needed.
  • Fixes YOLOE-26 prompt-free RKNN export for detection and segmentation.
  • Corrects pose loss selection for end-to-end models using non-Pose26 heads.
  • Prevents C2PSA failures when its channel count is below 64.

  • Fixes MuSGD handling of custom model head locations and higher-rank parameters.

  • Performance and stability improvements ⚡

  • Vectorizes Deep OC-SORT global motion compensation operations.
  • Reduces temporary memory usage and plotting time for segmentation masks by processing them in row bands.
  • Keeps accumulated heatmap overlays visible when a tracking frame temporarily contains no detections.
  • Prevents grayscale video stream failures from killing readers or crashing inference.
  • Makes mixed-text ordering deterministic across processes and distributed workers.

  • Keeps profiler samples when runtime variance reaches zero.

  • Documentation consistency and accuracy 📚

  • Establishes one canonical task and mode order across code, documentation, tables, and the Ultralytics Platform.
  • Consolidates supported-task tables into a shared macro and reports model-family-specific support more accurately.
  • Corrects multiple docstrings, doctest examples, export claims, task descriptions, and API references.
  • Updates Hailo, QNN, GraphDef, RKNN, and other integration pages with clearer model and hardware limitations.

🎯 Purpose & Impact

  • For Qualcomm users: IQ-8275 and QCS8275 devices can now be targeted directly during QNN export, simplifying deployment to Dragonwing hardware. Hardware-side BSP compatibility must still be verified.
  • For model developers: Heatmaps provide a clearer way to understand model decisions, while corrected FLOPs estimates make performance comparisons more trustworthy.
  • For edge deployment: RKNN and QNN improvements increase export compatibility and improve quantized inference reliability on specialized accelerators.
  • For training workflows: MuSGD, pose-loss, C2PSA, and higher-rank tensor fixes reduce silent training errors and improve support for custom architectures.
  • For tracking and streaming applications: Video recovery, heatmap persistence, and Deep OC-SORT updates improve robustness in real-world, imperfect inputs.
  • For documentation users: Shared and empirically validated compatibility tables make it easier to determine which model family, task, and deployment target is supported. 🚀

What's Changed

New Contributors

Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.4.112...v8.4.113

Source: README.md, updated 2026-07-30