| | --- |
| | license: cc-by-4.0 |
| | dataset_info: |
| | features: |
| | - name: id |
| | dtype: int64 |
| | - name: image |
| | dtype: image |
| | - name: objects |
| | list: |
| | - name: bb |
| | sequence: float64 |
| | - name: label |
| | dtype: string |
| | splits: |
| | - name: train |
| | num_bytes: 2216186843.984 |
| | num_examples: 20276 |
| | download_size: 2202918677 |
| | dataset_size: 2216186843.984 |
| | --- |
| | |
| | Kodytek P, Bodzas A and Bilik P. A large-scale image dataset of wood surface defects for automated vision-based quality control processes [version 2; peer review: 2 approved]. F1000Research 2022, 10:581 (https://doi.org/10.12688/f1000research.52903.2) |
| |
|
| | Bounding boxes only, semantic maps |
| |
|
| | All images are 2800 x 1024 pixels (width x height) |
| |
|
| | Images compressed using |
| | ```python |
| | PIL.Image.Image.save( |
| | format="JPEG", |
| | optimize=True, |
| | quality=50, |
| | ) |
| | ``` |
| |
|
| | Bounding boxes converted to YOLO format. |
| |
|
| | TODO: loader script, preview, semantic maps |
| |
|
