Skip to content

Reference for ultralytics/models/yolo/classify/predict.py

Note

This file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/classify/predict.py. If you spot a problem please help fix it by contributing a Pull Request 🛠️. Thank you 🙏!


ultralytics.models.yolo.classify.predict.ClassificationPredictor 

ClassificationPredictor(cfg=DEFAULT_CFG, overrides=None, _callbacks=None)

Bases: BasePredictor

A class extending the BasePredictor class for prediction based on a classification model.

This predictor handles the specific requirements of classification models, including preprocessing images and postprocessing predictions to generate classification results.

Attributes:

Name Type Description
args dict

Configuration arguments for the predictor.
_legacy_transform_name str

Name of the legacy transform class for backward compatibility.

Methods:

Name Description
preprocess

Convert input images to model-compatible format.
postprocess

Process model predictions into Results objects.
Notes
  • Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'.

Examples:

>>> from ultralytics.utils import ASSETS
>>> from ultralytics.models.yolo.classify import ClassificationPredictor
>>> args = dict(model="yolo11n-cls.pt", source=ASSETS)
>>> predictor = ClassificationPredictor(overrides=args)
>>> predictor.predict_cli()
Source code in ultralytics/models/yolo/classify/predict.py 
38
39
40
41
42
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
    """Initialize the ClassificationPredictor with the specified configuration and set task to 'classify'."""
    super().__init__(cfg, overrides, _callbacks)
    self.args.task = "classify"
    self._legacy_transform_name = "ultralytics.yolo.data.augment.ToTensor"

postprocess 

postprocess(preds, img, orig_imgs)

Process predictions to return Results objects with classification probabilities.

Parameters:

Name Type Description Default
preds Tensor

Raw predictions from the model.

 required
img Tensor

Input images after preprocessing.

 required
orig_imgs List[ndarray] | Tensor

Original images before preprocessing.

 required

Returns:

Type Description
List[Results]

List of Results objects containing classification results for each image.
Source code in ultralytics/models/yolo/classify/predict.py 
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
def postprocess(self, preds, img, orig_imgs):
    """
    Process predictions to return Results objects with classification probabilities.

    Args:
        preds (torch.Tensor): Raw predictions from the model.
        img (torch.Tensor): Input images after preprocessing.
        orig_imgs (List[np.ndarray] | torch.Tensor): Original images before preprocessing.

    Returns:
        (List[Results]): List of Results objects containing classification results for each image.
    """
    if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
        orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)

    preds = preds[0] if isinstance(preds, (list, tuple)) else preds
    return [
        Results(orig_img, path=img_path, names=self.model.names, probs=pred)
        for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0])
    ]

preprocess 

preprocess(img)

Convert input images to model-compatible tensor format with appropriate normalization.

Source code in ultralytics/models/yolo/classify/predict.py 
44
45
46
47
48
49
50
51
52
53
54
55
56
57
def preprocess(self, img):
    """Convert input images to model-compatible tensor format with appropriate normalization."""
    if not isinstance(img, torch.Tensor):
        is_legacy_transform = any(
            self._legacy_transform_name in str(transform) for transform in self.transforms.transforms
        )
        if is_legacy_transform:  # to handle legacy transforms
            img = torch.stack([self.transforms(im) for im in img], dim=0)
        else:
            img = torch.stack(
                [self.transforms(Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))) for im in img], dim=0
            )
    img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device)
    return img.half() if self.model.fp16 else img.float()  # uint8 to fp16/32



📅 Created 1 year ago ✏️ Updated 6 months ago
jk4e glenn-jocher Burhan-Q