CeyMo is a novel benchmark dataset for road marking detection which covers a wide variety of challenging urban, sub-urban and rural road scenarios. The dataset consists of 2887 total images of 1920 × 1080 resolution with 4706 road marking instances belonging to 11 classes. The test set is divided into six categories: normal, crowded, dazzle light, night, rain and shadow.
Variants: CeyMo
This dataset is used in 1 benchmark:
| Task | Model | Paper | Date |
|---|---|---|---|
| 2D Object Detection | TransMind | Open-TransMind: A New Baseline and … | 2023-04-12 |
| 2D Object Detection | YOLOv7 | YOLOv7: Trainable bag-of-freebies sets new … | 2022-07-06 |
| 2D Object Detection | TOOD | TOOD: Task-aligned One-stage Object Detection | 2021-08-17 |
| 2D Object Detection | YOLOX | YOLOX: Exceeding YOLO Series in … | 2021-07-18 |
| 2D Object Detection | Sparse R-CNN | Sparse R-CNN: End-to-End Object Detection … | 2020-11-25 |
Recent papers with results on this dataset: