| from ultralytics import YOLO | |
| # Load a model | |
| model = YOLO("best.pt") # load a pretrained model (recommended for training) | |
| # Use the model | |
| # model.train(data="/data.yaml", epochs=3) # train the model | |
| # metrics = model.val() # evaluate model performance on the validation set | |
| results = model("data/Screenshot 2023-10-21 at 14.28.32.png", stream=True, show=True, save=True, conf=0.7) # predict on an image | |
| # path = model.export(format="onnx") # export the model to ONNX format | |
| # Process results generator | |
| for result in results: | |
| boxes = result.boxes # Boxes object for bbox outputs | |
| masks = result.masks # Masks object for segmentation masks outputs | |
| keypoints = result.keypoints # Keypoints object for pose outputs | |
| probs = result.probs # Probs object for classification outputs |