Step 1: Prepare Your Data

Classification

Organize images into class folders:

data/
├── train/
│   ├── class_a/
│   │   ├── img001.jpg
│   │   └── img002.jpg
│   └── class_b/
│       ├── img003.jpg
│       └── img004.jpg
└── val/
    ├── class_a/
    │   └── img005.jpg
    └── class_b/
        └── img006.jpg

from cv_toolkit.datasets import ImageFolderDataset

train_dataset = ImageFolderDataset(
    root="data/train",
    image_size=(224, 224),
)
print(f"Classes: {train_dataset.classes}")
print(f"Samples: {len(train_dataset)}")

Detection

Use COCO-format JSON annotations with one annotation file per split:

data/
├── images/
│   ├── train/
│   └── val/
└── annotations/
    ├── train.json    # COCO format
    └── val.json

from cv_toolkit.datasets import DetectionDataset

train_dataset = DetectionDataset(
    images_dir="data/images/train",
    annotations_file="data/annotations/train.json",
    annotation_format="coco",
)

Segmentation

Segmentation masks are single-channel images where each pixel value is a class ID:

data/
├── images/
│   ├── train/
│   └── val/
└── masks/
    ├── train/     # Same filenames as images, PNG format
    └── val/

Included with Computer Vision Toolkit

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