Datasets:
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# Datasets-Structure
**(YOLOv8 format)**
The Yolov8 dataset for segmentation is usually structured as follows:
```
yolo_dataset/
│
├── train/
│ ├── images/
│ │ └── 🖼️ img_n # Example image
│ │
│ └── labels/
│ └── 📄 img_n_labels.txt # Example labels file
│
├── valid/
│ │ ... (similar)
│
└── 📄 data.yaml
```
Each ```img_x_labels.txt``` file contains multiple annotations (one per line) with corresponding class ID and segmentation coordinates:
`<class-index> <x1> <y1> <x2> <y2> ... <xn> <yn>`
The file `data.yaml` contains keys such as:
- names (the class names)
- nc (number or classes)
- train (path/to/train/images/)
- val (path/to/val/images/)
**(COCO Instance Segmentation format)**
```
coco_dataset/
│
├── train/
│ ├── 🖼️ img_n # Example image
│ └── 📄 annotations.json # The annotations json file
│
└── valid/
└── ... (similar)
```
The annotations json file contains a dictionary of lists:
- images (a list of dictionaries)
- id - image ID
- file_name
- height
- width
- annotations (a list of dictionaries)
- id
- image_id
- category_id
- bbox
- area
- segmentation (a segmentation polygon)
- iscrowd
- categories (a list of dictionaries)
- id
- name |