| --- |
| license: cc0-1.0 |
| task_categories: |
| - object-detection |
| tags: |
| - welding |
| - defect-detection |
| - manufacturing |
| - coco |
| - yolo |
| pretty_name: Welding Defect Object Detection (YOLO + COCO) |
| --- |
| |
| # Welding Defect Object Detection |
|
|
| 2,028 annotated images of welds for defect detection, in **both YOLO and COCO |
| formats**. Three classes: |
|
|
| | id (YOLO / COCO) | name | |
| |---|---| |
| | 0 / 1 | Bad Weld | |
| | 1 / 2 | Good Weld | |
| | 2 / 3 | Defect | |
|
|
| ## Splits |
|
|
| | split | images | annotations | |
| |---|---|---| |
| | train | 1,619 | 4,583 | |
| | valid | 283 | 802 | |
| | test | 126 | 301 | |
|
|
| ## Layout |
|
|
| ``` |
| ├── data.yaml # YOLO class names + split paths |
| ├── train|valid|test/ |
| │ ├── images/ # .jpg |
| │ └── labels/ # YOLO .txt (class cx cy w h, normalized) |
| └── coco/ |
| ├── train.json # COCO detection format |
| ├── valid.json |
| └── test.json |
| ``` |
|
|
| ## COCO conversion notes |
|
|
| The `coco/` jsons were generated from the YOLO labels with the |
| [flux](https://github.com/rikkarth) YOLO→COCO converter: |
|
|
| - bbox = `[x_min, y_min, width, height]`, float pixels |
| - boxes clamped to image bounds |
| - category ids are one-based (YOLO class 0 → COCO id 1) |
| - annotation count parity verified: 5,686 YOLO label lines → 5,686 COCO annotations |
|
|
| ## Source & license |
|
|
| Original dataset published on Kaggle by sukmaadhiwijaya as |
| [Welding Defect - Object Detection](https://www.kaggle.com/datasets/sukmaadhiwijaya/welding-defect-object-detection) |
| under **CC0: Public Domain**. This mirror adds the COCO-format annotations. |
|
|
