---
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.