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Clarify tt100k uses 221 annotation categories with 45 trainable classes (#24234)

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Co-authored-by: Jing Qiu <61612323+Laughing-q@users.noreply.github.com>
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Murat Raimbekov 2026-04-15 15:49:00 +06:00 committed by GitHub
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docs/en/datasets/detect/tt100k.md
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@ -8,7 +8,7 @@ keywords: TT100K, Tsinghua-Tencent 100K, traffic sign detection, YOLO26, dataset
The [Tsinghua-Tencent 100K (TT100K)](https://cg.cs.tsinghua.edu.cn/traffic-sign/) is a large-scale traffic sign benchmark dataset created from 100,000 Tencent Street View panoramas. This dataset is specifically designed for traffic sign detection and classification in real-world conditions, providing researchers and developers with a comprehensive resource for building robust traffic sign recognition systems.
The dataset contains **100,000 images** with over **30,000 traffic sign instances** across **221 different categories**. These images capture large variations in illuminance, weather conditions, viewing angles, and distances, making it ideal for training models that need to perform reliably in diverse real-world scenarios.
The dataset contains **100,000 images** with over **30,000 traffic sign instances** across **221 annotation categories**. The original paper applies a 100-instance threshold per class for supervised training, yielding a commonly used **45-class** subset; however, the provided Ultralytics dataset configuration retains all **221 annotated categories**, many of which are very sparse. These images capture large variations in illuminance, weather conditions, viewing angles, and distances, making it ideal for training models that need to perform reliably in diverse real-world scenarios.
This dataset is particularly valuable for: