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Converting COCO Labels to YOLOv8 Format

This tutorial walks you through converting object detection labels from the COCO format to the YOLOv8 format using Labelformat's CLI and Python API.

Prerequisites

  • Labelformat Installed: Follow the Installation Guide.
  • COCO Dataset: Ensure you have a COCO-formatted dataset ready for conversion.

Step 1: Prepare Your Dataset

Organize your dataset with the following structure:

project/
├── coco-labels/
│   └── train.json
├── images/
│   ├── image1.jpg
│   └── image2.jpg

Ensure that train.json contains the COCO annotations and that all images are located in the images/ directory.

Step 2: Using the CLI for Conversion

Open your terminal and navigate to the project/ directory.

Run the following command to convert COCO labels to YOLOv8:

labelformat convert \
    --task object-detection \
    --input-format coco \
    --input-file coco-labels/train.json \
    --output-format yolov8 \
    --output-file yolo-labels/data.yaml \
    --output-split train

Explanation of the Command:

  • --task object-detection: Specifies the task type.
  • --input-format coco: Defines the input label format.
  • --input-file coco-labels/train.json: Path to the COCO annotations file.
  • --output-format yolov8: Desired output format.
  • --output-file yolo-labels/data.yaml: Path to save the YOLOv8 configuration file.
  • --output-split train: Data split label.

Step 3: Verify the Conversion

After running the command, your project structure should include:

project/
├── yolo-labels/
│   ├── data.yaml
│   └── labels/
│       ├── image1.txt
│       └── image2.txt
  • data.yaml: YOLOv8 configuration file containing category names and paths.
  • labels/: Directory containing YOLOv8-formatted label files.

Sample data.yaml: 

names:
  0: cat
  1: dog
  2: fish
nc: 3
path: .
train: images

Sample Label File (image1.txt):

2 0.8617 0.7308 0.0359 0.0433
0 0.8180 0.6911 0.0328 0.0793
  • Format: <category_id> <center_x> <center_y> <width> <height>
  • Coordinates: Normalized between 0 and 1.

Step 4: Using the Python API for Conversion

If you prefer using Python for more control, follow these steps.

4.1: Write the Conversion Script

Create a Python script named coco_to_yolov8.py with the following content:

from pathlib import Path
from labelformat.formats import COCOObjectDetectionInput, YOLOv8ObjectDetectionOutput

# Define input and output paths
coco_input_path = Path("coco-labels/train.json")
yolo_output_path = Path("yolo-labels/data.yaml")

# Initialize input and output classes
coco_input = COCOObjectDetectionInput(input_file=coco_input_path)
yolo_output = YOLOv8ObjectDetectionOutput(
    output_file=yolo_output_path,
    output_split="train"
)

# Perform the conversion
yolo_output.save(label_input=coco_input)

print("Conversion from COCO to YOLOv8 completed successfully!")

4.2: Execute the Script

Run the script using Python:

python coco_to_yolov8.py

Upon successful execution, you will see:

Conversion from COCO to YOLOv8 completed successfully!

Step 5: Integrate with Your Training Pipeline

Use the generated YOLOv8 labels (data.yaml and labels/ directory) to train your YOLOv8 models seamlessly.

Example YOLOv8 Training Command:

yolo detect train data=yolo-labels/data.yaml model=yolov8s.pt epochs=100 imgsz=640

Conclusion

You've successfully converted COCO labels to YOLOv8 format using both the CLI and Python API. Labelformat simplifies label format conversions, enabling efficient integration into your computer vision projects.

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