ultralytics 8.3.183

v8.3.183 - `ultralytics 8.3.183` New NDJSON dataset format support (#21747)

on Python PyPI

🌟 Summary

Train YOLO11 models directly from NDJSON datasets with one command — including automatic conversion and image downloading — plus improved YOLOE docs/examples and more reliable GPU CI on GitHub. 🚀

📊 Key Changes

• NDJSON training support (priority)
  • Built-in NDJSON→YOLO11 converter: convert_ndjson_to_yolo(ndjson_path, output_path=None) by @glenn-jocher
  • Trainer integration: pass an .ndjson file to model.train(data=...) and it auto-converts and generates data.yaml ✅
  • Fast, resumable downloads: fetches images from URLs with async parallel workers and progress bar ⚡
  • Rich annotation support: handles detection boxes, segments, pose, OBB, and classification; writes the first available type found 🧰
  • Docs updated with NDJSON format examples and advantages
• YOLOE improvements
  • Corrected pretrained checkpoints and a clearer prompt-free validation example with single_cls=True, plus quieter device selection logs by @ShuaiLYU 📚
• CI upgrades
  • Enabled GPU CI on T4 runners, switched to pip for reliability, conditional installs for heavy deps, and adjusted ONNX/TensorRT tests for stability 🔧

Minimal examples:

• Python:

  from ultralytics import YOLO
  model = YOLO("yolo11n.pt")
  model.train(data="path/to/dataset.ndjson", epochs=100, imgsz=640)

• CLI:

  yolo detect train data=path/to/dataset.ndjson model=yolo11n.pt epochs=100 imgsz=640

🎯 Purpose & Impact

• Simpler data onboarding: Use a single NDJSON file (with remote URLs) to kick off training — no manual conversion required. 🧩
• Faster, scalable pipelines: Async, parallel downloads and streaming-friendly NDJSON make large datasets more manageable. 🚄
• Broader task coverage: Works across detection, segmentation, pose, OBB, and classification in a unified flow. 🏷️
• Fewer mistakes: Updated YOLOE examples ensure correct weights and cleaner validation outputs, reducing user friction. ✅
• More reliable CI: GPU tests run on GitHub with better dependency handling, improving confidence in CUDA/ONNX/TensorRT paths. 🧪

See the Ultralytics Docs for details.

What's Changed

• Fix YOLOE prompt-free validation example in docs by @ShuaiLYU in #21745
• Enable GitHub GPU runner CI by @glenn-jocher in #21682
• ultralytics 8.3.183 New NDJSON dataset format support by @glenn-jocher in #21747

New Contributors

• @ShuaiLYU made their first contribution in #21745

Full Changelog: v8.3.182...v8.3.183