detect.py

##Train##
from ultralytics import YOLO

# Load a model
model = YOLO('yolov8n.yaml')  # build a new model from YAML
model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
model = YOLO('yolov8n.yaml').load('yolov8n.pt')  # build from YAML and transfer weights

# Train the model
results = model.train(data='coco128.yaml', epochs=100, imgsz=640)


##validate trained YOLOv8n model## 

# Load a model
model = YOLO('yolov8n.pt')  # load an official model
#model = YOLO('path/to/best.pt')  # load a custom model

# Validate the model
metrics = model.val()  # no arguments needed, dataset and settings remembered
metrics.box.map    # map50-95
metrics.box.map50  # map50
metrics.box.map75  # map75
metrics.box.maps   # a list contains map50-95 of each category


# Predict 
#Use a trained YOLOv8n model 
# to run predictions on images.
# Load a model
model = YOLO('yolov8n.pt')  # load an official model
#model = YOLO('path/to/best.pt')  # load a custom model

# Predict with the model
results = model('https://ultralytics.com/images/bus.jpg')  # predict on an image

#Export
#maybe try torch for format

# Load a model
model = YOLO('yolov8n.pt')  # load an official model
#model = YOLO('path/to/best.pt')  # load a custom trained model

# Export the model
model.export(format='onnx')