ultralytics 8.4.54

v8.4.54 - `ultralytics 8.4.54` Qualcomm QNN export (#24591)

on Python PyPI

🌟 Summary

Ultralytics v8.4.54 is led by a big new deployment feature: local Qualcomm QNN export for Snapdragon devices 📱⚡, plus several quality-of-life improvements for Solutions, export docs, tuning compatibility, and data-loading reliability.

📊 Key Changes

• 🚀 New qnn export format for Qualcomm Snapdragon devices

  • You can now export YOLO models to Qualcomm QNN with format="qnn".
  • This compiles a model locally into a QNN-ready package using ONNX Runtime’s QNN Execution Provider.
  • No Qualcomm account, no cloud upload, and no separate Qualcomm SDK download are required.
  • Works with Snapdragon targets including CPU, GPU, and Hexagon NPU paths.
  • Example:

    from ultralytics import YOLO
    
    model = YOLO("yolo26n.pt")
    model.export(format="qnn")

• 📦 New QNN backend support for inference/validation workflows

  • Exported QNN models can now fit into the normal Ultralytics workflow more cleanly.
  • QNN is registered as a supported export target, with dedicated backend and docs support.
  • End-to-end export is automatically disabled for QNN where unsupported, helping avoid invalid configurations.

• 📘 Major export documentation cleanup and standardization

  • Integration docs across many formats were reorganized into a clearer Export / Predict / Validate structure.
  • A full new guide for Qualcomm QNN was added.
  • This makes deployment paths easier to understand across formats like ONNX, TensorRT, OpenVINO, NCNN, TFLite, CoreML, and more.

• 🖼️ imgsz added to all Ultralytics Solutions

  • All Solutions now support the imgsz argument, giving users direct control over inference input size.
  • This also improves logging so reported input shapes better reflect the real model input.

• 🛠️ Better robustness for segmentation and image caching

  • Fixed semantic segmentation mask loading when masks appear as (H, W, 1) instead of plain single-channel arrays.
  • Fixed stale .npy image cache issues that could cause grayscale models to accidentally load RGB data.

• 🧠 Tracking utility optimization

  • Reduced some SciPy dependency usage in tracking internals by replacing parts with NumPy equivalents.
  • This helps simplify dependencies and improve maintainability.

• 🎯 Ray Tune compatibility improvement

  • Updated tuner trial ID handling to work correctly with Ray 2.7+ while keeping support for older versions.

• 🧾 Small but useful documentation updates

  • Validation docs now include show_labels and show_conf.
  • Prediction docs now explicitly list .jpeg2000 as a supported image format.

• 🔬 Semantic segmentation loss dtype fix

  • Improved dtype consistency in segmentation loss computation, especially helpful for mixed-precision training paths.

🎯 Purpose & Impact

• 📱 Big win for edge/mobile deployment

  • The new QNN export makes it much easier to deploy YOLO on Qualcomm Snapdragon hardware.
  • This is especially valuable for mobile, embedded, automotive, and low-power AI use cases.

• ⚡ Faster path to production on Snapdragon

  • Users can now go from a standard YOLO model to a Snapdragon-ready export without extra accounts or vendor tooling setup.
  • That lowers friction for developers evaluating on-device AI.

• 🧩 More consistent deployment experience

  • Standardized export docs make it easier for both beginners and experienced users to compare formats and follow the right steps.

• 🎛️ More control in packaged Solutions

  • Adding imgsz to Solutions gives users a simple way to trade off speed vs. accuracy, just like in normal YOLO inference.

• 🛡️ More reliable training and inference

  • The mask-loading and grayscale cache fixes reduce confusing failures caused by edge-case dataset formats or stale cache files.

• 🔄 Better compatibility with modern tooling

  • Ray Tune improvements help users keep hyperparameter tuning workflows working across newer environments.

• 👀 Cleaner debugging and validation visuals

  • The documented show_labels and show_conf options make validation outputs easier to customize for analysis.

Overall, v8.4.54 is a strong release for anyone interested in device deployment, especially on Qualcomm Snapdragon, while also tightening up reliability and usability across the broader Ultralytics ecosystem 🚀

What's Changed

• Docs: add show_labels and show_conf val args by @K-saif in #24571
• Scope scipy imports and optimize embedding_distance with NumPy by @Laughing-q in #24572
• Revert _gaussian_smooth changes by @Laughing-q in #24575
• Match aux_loss dtype to ce_loss in SemanticSegmentationLoss by @ahmet-f-gumustas in #24576
• Add imgsz support to all Ultralytics Solutions by @Laughing-q in #24579
• Fix: Squeeze single-channel 3D mask arrays from patched cv2.imread by @Laughing-q in #24582
• Fix grayscale test failure from stale .npy cache files by @Laughing-q in #24583
• Add jpeg2000 to predict images formats table by @raimbekovm in #24588
• Fix tuner trial ID retrieval for Ray 2.7+ compatibility by @lmycross in #24590
• ultralytics 8.4.54 Qualcomm QNN export by @glenn-jocher in #24591

Full Changelog: v8.4.53...v8.4.54