ultralytics 8.4.143

v8.4.143 - Add INT8 quantization-aware training via quantize=8 in train mode (#26083)

on Python PyPI

🌟 Summary

v8.4.143 introduces INT8 quantization-aware training for YOLO26, improves deployment and evaluation workflows, and delivers a broad documentation and integration refresh. 🚀

📊 Key Changes

• 🧠 INT8 quantization-aware training (QAT) — PR #26083

  • Adds support for quantize=8 during train mode.
  • Fine-tunes pretrained weights while simulating INT8 quantization, allowing the model to adapt before deployment rather than being quantized only after training.
  • Stores calibrated quantization ranges in the checkpoint and supports direct export to ONNX and TensorRT with Q/DQ nodes—without requiring calibration data during export.
  • Reuses the existing quantize argument and adds no new public API or dependency.
  • Particularly useful for deploying YOLO26 models to resource-constrained edge hardware.

• 📈 More complete validation metrics — PR #24489

  • Adds per-class AP75 values to validation summaries, DataFrame outputs, CSV files, and JSON exports.
  • Applies consistently to detection, segmentation, pose, and OBB tasks.

• 🚀 Improved YOLO26 deployment workflows

  • DeepStream documentation now uses the native Ultralytics exporter instead of a separate third-party script.
  • Clarifies when to use the NMS-free head with nms=False and updates Triton, DALI, SAM, and C++ examples accordingly.
  • Adds documentation for Apple’s Core AI export target and backend.
  • Corrects quantization guidance for formats that support only specific precisions.

• 🛠️ Training and model lifecycle fixes

  • Corrects pretrained-model handling after reset_weights(), during resume operations, and in multi-dataset or distributed training.
  • Preserves Platform model URIs when training through Python.
  • Keeps prediction output directories when copying or converting Results objects.
  • Removes stale SAM3 decoder coordinate caches, improving reliability across device and precision changes.

• 🌍 Platform and authentication updates

  • Uses the Ultralytics Platform SDK for login, model downloads, dataset exports, and training resource access.
  • Aligns Python 3.11+ installations with the shared-login SDK requirements while preserving local workflows on older Python versions.

• 📚 Extensive documentation improvements

  • Reviews and corrects examples, CLI syntax, export details, tracker behavior, dataset layouts, image decoding, hardware integrations, and model descriptions.
  • Expands YOLO26 depth-estimation dataset documentation with new FAQs and dataset guidance.
  • Updates Ultralytics Platform links across dataset and training pages.
  • Documents improved Albumentations support for boxes, polygons, keypoints, depth maps, and semantic masks.
  • Refreshes DeepStream, OpenVINO, Core AI, Edge TPU, Ray Tune, Jetson, and other integration guides.

🎯 Purpose & Impact

• ⚡ Better edge performance: QAT can help INT8-deployed models retain more accuracy while benefiting from lower memory usage, faster inference, and reduced power consumption.
• 📦 Simpler deployment: A QAT-trained checkpoint can be exported to ONNX or TensorRT without a separate calibration dataset, reducing deployment steps.
• 📊 Better model analysis: Per-class AP75 makes it easier to identify which categories perform well or need additional training.
• 🔌 Broader hardware support: Updated exporters and documentation make YOLO26 easier to deploy with DeepStream, TensorRT, Core AI, OpenVINO, Platform, and other environments.
• 🧰 More reliable training: Pretrained-state and resume fixes reduce the risk of unintentionally training from random initialization or using the wrong checkpoint.
• ✅ Clearer user experience: Corrected documentation and examples should reduce setup errors, especially for new users and deployment engineers.

What's Changed

• Include per-class AP75 in validation summaries and exports by @woreom in #24489
• Review docs pages for correctness, consistency, and Platform links by @glenn-jocher in #26070
• Drop the FP32 promise from the export quantize hint on formats that cannot export FP32 by @raimbekovm in #26080
• Export YOLO26 for DeepStream with the Ultralytics exporter by @raimbekovm in #26077
• Honor the pretrained argument after reset_weights() and in MultiTrainer by @raimbekovm in #26084
• Preserve prediction save directory in Results copies by @aswanth-07 in #26074
• Select the NMS-free head explicitly where docs and examples still assume it by @raimbekovm in #26072
• Remove stale SAM3 decoder coordinate caches by @tritsystem in #26075
• Update docs for the max_det dataset raise, Core AI export and spatial Albumentations by @raimbekovm in #26078
• Use Platform SDK for login and training resource downloads by @glenn-jocher in #26087
• Require Platform SDK >=0.1.32 for shared login by @glenn-jocher in #26088
• Add INT8 quantization-aware training via quantize=8 in train mode by @Bovey0809 in #26083

New Contributors

• @tritsystem made their first contribution in #26075
• @woreom made their first contribution in #24489

Full Changelog: v8.4.142...v8.4.143