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# Install required package
# pip install ultralytics
from ultralytics import YOLO
# Dataset structure expected:
# ├── dataset/
# │ ├── train/
# │ │ ├── images/
# │ │ └── labels/
# │ ├── valid/
# │ │ ├── images/
# │ │ └── labels/
# │ └── test/
# │ ├── images/
# │ └── labels/
# data.yaml example:
# path: /path/to/dataset
# train: train/images
# val: valid/images
# test: test/images
# names:
# 0: class1
# 1: class2
# ...
def train_yolov8():
# Load the YOLOv8 Large model
model = YOLO('yolov8l.pt') # pretrained model
# Train the model
results = model.train(
data='data.yaml',
epochs=100,
batch=16,
imgsz=640,
device='0', # 'cpu' or '0' for GPU
name='yolov8l_custom',
optimizer='Adam',
lr0=0.001,
warmup_epochs=3,
augment=True,
patience=50,
pretrained=True
)
# Validate the model
metrics = model.val() # Validate on validation set
print(f"Validation [email protected]: {metrics.box.map}")
# Test the model (optional)
test_model = YOLO('runs/detect/yolov8l_custom/weights/best.pt')
test_metrics = test_model.val(data='data.yaml', split='test')
print(f"Test [email protected]: {test_metrics.box.map}")
# Export to ONNX format (optional)
model.export(format='onnx')
if __name__ == '__main__':
train_yolov8() |