ultralytics 8.3.250

v8.3.250 - `ultralytics 8.3.250` Tsinghua-Tencent 100K dataset (#22892)

on Python PyPI

🌟 Summary (single-line synopsis)

Ultralytics v8.3.250 adds out-of-the-box support for the TT100K traffic sign dataset 🚦—plus smoother Ultralytics HUB dataset handling, better run directory behavior, and a few quality/build/docs fixes 🛠️📚

📊 Key Changes

• 🚦 New TT100K dataset integration (PR #22892 by @PrashantDixit0)
  • Adds ultralytics/cfg/datasets/TT100K.yaml with 221 classes and full dataset metadata.
  • Includes an auto download + conversion pipeline that fetches TT100K and converts its annotations into YOLO-format labels automatically.
  • Adds new docs page and navigation entries for TT100K:
    • TT100K dataset page
• 🌐 Improved Ultralytics HUB dataset URI support + NDJSON auto-conversion (PR #23146 by @glenn-jocher)
  • Training now supports ul://.../datasets/... sources by resolving them locally first, then performing NDJSON → YOLO conversion automatically.
  • Smarter --project path handling for “nested” names like user/project, placing outputs under the normal runs directory.
  • Updates platform webhook base URL used by the platform callback resolver.
• 📚 Better Kaggle install & troubleshooting docs (PR #22970 by @PrashantDixit0)
  • Adds a clear Kaggle Installation section (internet toggle, install commands, conflict fixes) to reduce setup friction.
• 🧩 Metrics bugfix: ConfusionMatrix default type mismatch (PR #23138 by @raimbekovm)
  • Fixes ConfusionMatrix(names=...) default from [] to {} to match its annotated type and avoid mutable-default pitfalls.
• 🛠️ C++ ONNXRuntime example build improvement (PR #23137 by @omar-A-hassan)
  • Respects user-provided ONNXRUNTIME_ROOT in CMake (so custom install paths work as documented).
• 🔒 Dependency update (PR #23143 by @dependabot[bot])
  • Bumps urllib3 in the RT-DETR ONNXRuntime Python example requirements.

🎯 Purpose & Impact

• 🚦 Easier traffic sign training & benchmarking with TT100K
  • You can train YOLO models on TT100K without manually downloading, unpacking, or rewriting annotations—ideal for autonomous driving / ADAS research and small-object detection experiments.
  • Example: yolo detect train data=TT100K.yaml model=yolo11n.pt 🏁
• 🌐 More seamless Ultralytics HUB workflows
  • If you use Ultralytics HUB datasets (via ul://), training should now “just work” even when the dataset starts as NDJSON—less manual conversion and fewer path surprises 📦✅
• 📁 Cleaner experiment organization
  • Nested --project values won’t accidentally create confusing folder structures; runs stay grouped under the expected task directory 🗂️
• 📚 Faster onboarding on Kaggle
  • Clear install + troubleshooting steps reduce time lost to notebook dependency conflicts and missing internet settings ⚡
• 🧩 Fewer edge-case bugs and smoother builds
  • Confusion matrix initialization is safer, and the ONNXRuntime C++ example is easier to compile in custom environments 🧰

What's Changed

• Fix type mismatch in ConfusionMatrix default parameter by @raimbekovm in #23138
• fix: respect user-provided ONNXRUNTIME_ROOT in CMakeLists.txt by @omar-A-hassan in #23137
• Bump urllib3 from 2.6.0 to 2.6.3 in /examples/RTDETR-ONNXRuntime-Python in the pip group across 1 directory by @dependabot[bot] in #23143
• docs: installation guide in Kaggle Notebook by @PrashantDixit0 in #22970
• Update platform dir handling by @glenn-jocher in #23146
• ultralytics 8.3.250 Tsinghua-Tencent 100K dataset by @PrashantDixit0 in #22892

New Contributors

• @omar-A-hassan made their first contribution in #23137

Full Changelog: v8.3.249...v8.3.250