Yolov5_Custom

## Create a Custom Python Script:

# Filename: custom_config.py
import os

# Define your class names
class_names = ['class1', 'class2', 'class3']

# Specify the path to your dataset
data_dir = '/path/to/dataset'

# Create a list of image and annotation paths
images = [os.path.join(data_dir, 'images', img) for img in os.listdir(os.path.join(data_dir, 'images')) if img.endswith('.jpg')]
annotations = [os.path.join(data_dir, 'annotations', ann) for ann in os.listdir(os.path.join(data_dir, 'annotations')) if ann.endswith('.txt')]

# Create a dictionary for the data configuration
data = {
    'train': images,
    'val': [],  # You can split your dataset into train and validation
    'nc': len(class_names),
    'names': class_names
}
###########################
# In your training script (e.g., train.py for YOLOv5), import the custom_config.py and use the data dictionary.

# Filename: train.py
from custom_config import data, class_names
from pathlib import Path
import yaml

# Create a directory for saving YOLOv5 configuration
save_dir = Path('yolov5_data')
save_dir.mkdir(parents=True, exist_ok=True)

# Save the custom data configuration as a YAML file
with open(save_dir / 'custom_data.yaml', 'w') as f:
    yaml.dump(data, f)

# Now, you can use this custom_data.yaml in YOLOv5 training
!python train.py --data save_dir/custom_data.yaml --cfg models/yolov5s.yaml --batch-size 16 --epochs 50