ultralytics 8.4.44

v8.4.44 - `ultralytics 8.4.44` Apply `pretrained` arg across model trainers (#24374)

on Python PyPI

🌟 Summary

Ultralytics v8.4.44 improves training control and deployment reliability, led by a key fix that makes the pretrained setting behave consistently across trainers 🔧🚀.

📊 Key Changes

• (Top priority) pretrained argument now works consistently in training flows (@glenn-jocher) ✅

  • pretrained=False is now properly respected even when training from a loaded .pt checkpoint.
  • You can now pass a custom pretrained weights path (string) to override checkpoint weights while still reusing the checkpoint’s model config.
  • Removed older classification-only reset logic because shared trainer setup now handles this behavior consistently.

• Export improvements for INT8 workflows 📦

  • Added data as a valid export argument for more backends (including TensorRT, OpenVINO, CoreML, TFLite, TF.js, MNN, IMX, and others), reducing “unsupported argument” friction.
  • Better support for dataset-aware calibration/export pipelines.

• Jetson + TensorRT stability updates 🤖

  • JetPack 6 Docker now upgrades TensorRT from 10.3 to 10.7 to fix YOLO26 INT8 build issues.
  • end2end auto-disable logic for INT8 export is now narrowly targeted to the known problematic combo (JetPack 6 + TensorRT 10.3.0), instead of broader disabling.

• TensorRT version guardrails updated 🛡️

  • Blocks problematic TensorRT 10.2.0 (replacing older 10.1.0 exclusion) in loading/export checks.
  • Centralized TensorRT checks for more consistent setup behavior.

• Documentation updates 📚

  • Hyperparameter tuning guide now matches actual tuner behavior (including crossover usage and clearer fitness/result interpretation).
  • New Ultralytics Platform dataset Clustering docs: interactive 2D similarity view for finding duplicates, outliers, and data patterns faster.

🎯 Purpose & Impact

• More predictable training outcomes 🎯
  Users can trust that pretrained=False truly starts from random initialization, and pretrained="path/to/weights.pt" is honored correctly.

• Easier experiment control and reproducibility 🔁
  Reusing checkpoint configs while controlling weight initialization makes transfer learning and ablation experiments cleaner and less error-prone.

• Smoother deployment on edge devices ⚡
  Jetson/TensorRT fixes reduce export failures, especially for INT8 YOLO26 workflows.

• Fewer environment-related surprises 🧩
  TensorRT version protections help avoid known broken combinations before they cause runtime/export errors.

• Better usability for a broad audience 🌍
  Clearer docs and improved Platform dataset tooling (clustering) help both advanced practitioners and newer users work faster with higher confidence.

What's Changed

• Exclude tensorrt==10.2.0 to fix libnvinfer_builder_resource_win.so.10.2.0 error by @lakshanthad in #24367
• Auto-update tensorrt from 10.3 to 10.7 on JetPack 6 systems to fix YOLO26 int8 build issues by @lakshanthad in #24368
• Update hyperparameter tuning guide to match current tuner behaviour by @raimbekovm in #24372
• Add data argument to valid args for INT8 export formats by @lakshanthad in #24362
• Docs: Datasets Clustering by @sergiuwaxmann in #24376
• ultralytics 8.4.44 Apply pretrained arg across model trainers by @glenn-jocher in #24374

Full Changelog: v8.4.43...v8.4.44