coco2yolo-kaggle 0.1.0

Convert COCO dataset to YOLO format in Kaggle environment

COCO2YOLO Kaggle

A tool for converting COCO dataset to YOLO format in Kaggle environment. This tool is based on ultralytics/JSON2YOLO and optimized for Kaggle environment.

Features

• Automatically convert COCO JSON annotations to YOLO format
• Intelligently handle Kaggle storage space limitations
• Support parallel file operations for faster processing
• Support segment annotations
• Flexible commands for different operations

Installation

pip install coco2yolo-kaggle

Usage

Python API

Label Conversion Only

from coco2yolo_kaggle import convert_coco_labels

# Convert labels only
labels_dir = convert_coco_labels(
    json_dir="/kaggle/input/coco-2017-dataset/coco2017/annotations",
    output_dir="/kaggle/working/coco_yolo",
    use_segments=True,
    cls91to80=True
)

File Copy Only

from coco2yolo_kaggle import copy_dataset

# Copy dataset files to final destination
dataset_path = copy_dataset(
    json_dir="/kaggle/input/coco-2017-dataset/coco2017/annotations",
    output_dir="/kaggle/working/coco_yolo",
    final_dest="/kaggle/tmp/COCO2017",
    max_workers=8
)

Complete Conversion Process

from coco2yolo_kaggle import convert_coco_dataset

# Complete process (both conversion and copying)
dataset_path = convert_coco_dataset(
    json_dir="/kaggle/input/coco-2017-dataset/coco2017/annotations",
    output_dir="/kaggle/working/coco_yolo",
    final_dest="/kaggle/tmp/COCO2017",
    use_segments=True,
    cls91to80=True,
    max_workers=8,
    copy_files=True  # Set to False to skip copying files
)

Command Line Usage

Convert Labels Only (Default Mode)

coco2yolo-kaggle --json-dir=/kaggle/input/coco-2017-dataset/coco2017/annotations --output-dir=/kaggle/working/coco_yolo

Copy Files Only

coco2yolo-kaggle --mode=copy --json-dir=/kaggle/input/coco-2017-dataset/coco2017/annotations --output-dir=/kaggle/working/coco_yolo --final-dest=/kaggle/tmp/COCO2017

Complete Process

coco2yolo-kaggle --mode=all --json-dir=/kaggle/input/coco-2017-dataset/coco2017/annotations --output-dir=/kaggle/working/coco_yolo --final-dest=/kaggle/tmp/COCO2017

Kaggle Example Code

# 1. Install the package
!pip install coco2yolo-kaggle

# 2. Convert labels only
!coco2yolo-kaggle --json-dir=/kaggle/input/coco-2017-dataset/coco2017/annotations --output-dir=/kaggle/working/coco_yolo

# 3. Copy dataset files (if needed)
!coco2yolo-kaggle --mode=copy --json-dir=/kaggle/input/coco-2017-dataset/coco2017/annotations --output-dir=/kaggle/working/coco_yolo --final-dest=/kaggle/tmp/COCO2017

# 4. Train a YOLOv8 model
!pip install ultralytics
from ultralytics import YOLO

# Create dataset configuration file
%%writefile coco.yaml
path: /kaggle/tmp/COCO2017
train: images/train2017
val: images/val2017
nc: 80
names: ['person', 'bicycle', 'car', ... ] # Complete 80 class names

# Train the model
model = YOLO('yolov8n.pt')  # Use nano model
results = model.train(data='coco.yaml', epochs=3, imgsz=640)

Acknowledgements

This tool is based on ultralytics/JSON2YOLO, thanks to the original authors' contributions.