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

Note

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


ultralytics.models.yolo.classify.val.ClassificationValidator 

ClassificationValidator(
    dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None
)

Bases: BaseValidator

A class extending the BaseValidator class for validation based on a classification model.

This validator handles the validation process for classification models, including metrics calculation, confusion matrix generation, and visualization of results.

Attributes:

Name Type Description
targets List[Tensor]

Ground truth class labels.
pred List[Tensor]

Model predictions.
metrics ClassifyMetrics

Object to calculate and store classification metrics.
names dict

Mapping of class indices to class names.
nc int

Number of classes.
confusion_matrix ConfusionMatrix

Matrix to evaluate model performance across classes.

Methods:

Name Description
get_desc

Return a formatted string summarizing classification metrics.
init_metrics

Initialize confusion matrix, class names, and tracking containers.
preprocess

Preprocess input batch by moving data to device.
update_metrics

Update running metrics with model predictions and batch targets.
finalize_metrics

Finalize metrics including confusion matrix and processing speed.
postprocess

Extract the primary prediction from model output.
get_stats

Calculate and return a dictionary of metrics.
build_dataset

Create a ClassificationDataset instance for validation.
get_dataloader

Build and return a data loader for classification validation.
print_results

Print evaluation metrics for the classification model.
plot_val_samples

Plot validation image samples with their ground truth labels.
plot_predictions

Plot images with their predicted class labels.

Examples:

>>> from ultralytics.models.yolo.classify import ClassificationValidator
>>> args = dict(model="yolo11n-cls.pt", data="imagenet10")
>>> validator = ClassificationValidator(args=args)
>>> validator()
Notes

Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'.

Source code in ultralytics/models/yolo/classify/val.py 
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def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
    """Initialize ClassificationValidator with dataloader, save directory, and other parameters."""
    super().__init__(dataloader, save_dir, pbar, args, _callbacks)
    self.targets = None
    self.pred = None
    self.args.task = "classify"
    self.metrics = ClassifyMetrics()

build_dataset 

build_dataset(img_path)

Create a ClassificationDataset instance for validation.

Source code in ultralytics/models/yolo/classify/val.py 
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def build_dataset(self, img_path):
    """Create a ClassificationDataset instance for validation."""
    return ClassificationDataset(root=img_path, args=self.args, augment=False, prefix=self.args.split)

finalize_metrics 

finalize_metrics(*args, **kwargs)

Finalize metrics including confusion matrix and processing speed.

Source code in ultralytics/models/yolo/classify/val.py 
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def finalize_metrics(self, *args, **kwargs):
    """Finalize metrics including confusion matrix and processing speed."""
    self.confusion_matrix.process_cls_preds(self.pred, self.targets)
    if self.args.plots:
        for normalize in True, False:
            self.confusion_matrix.plot(
                save_dir=self.save_dir, names=self.names.values(), normalize=normalize, on_plot=self.on_plot
            )
    self.metrics.speed = self.speed
    self.metrics.confusion_matrix = self.confusion_matrix
    self.metrics.save_dir = self.save_dir

get_dataloader 

get_dataloader(dataset_path, batch_size)

Build and return a data loader for classification validation.

Source code in ultralytics/models/yolo/classify/val.py 
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def get_dataloader(self, dataset_path, batch_size):
    """Build and return a data loader for classification validation."""
    dataset = self.build_dataset(dataset_path)
    return build_dataloader(dataset, batch_size, self.args.workers, rank=-1)

get_desc 

get_desc()

Return a formatted string summarizing classification metrics.

Source code in ultralytics/models/yolo/classify/val.py 
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def get_desc(self):
    """Return a formatted string summarizing classification metrics."""
    return ("%22s" + "%11s" * 2) % ("classes", "top1_acc", "top5_acc")

get_stats 

get_stats()

Calculate and return a dictionary of metrics by processing targets and predictions.

Source code in ultralytics/models/yolo/classify/val.py 
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def get_stats(self):
    """Calculate and return a dictionary of metrics by processing targets and predictions."""
    self.metrics.process(self.targets, self.pred)
    return self.metrics.results_dict

init_metrics 

init_metrics(model)

Initialize confusion matrix, class names, and tracking containers for predictions and targets.

Source code in ultralytics/models/yolo/classify/val.py 
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def init_metrics(self, model):
    """Initialize confusion matrix, class names, and tracking containers for predictions and targets."""
    self.names = model.names
    self.nc = len(model.names)
    self.confusion_matrix = ConfusionMatrix(nc=self.nc, conf=self.args.conf, task="classify")
    self.pred = []
    self.targets = []

plot_predictions 

plot_predictions(batch, preds, ni)

Plot images with their predicted class labels and save the visualization.

Source code in ultralytics/models/yolo/classify/val.py 
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def plot_predictions(self, batch, preds, ni):
    """Plot images with their predicted class labels and save the visualization."""
    plot_images(
        batch["img"],
        batch_idx=torch.arange(len(batch["img"])),
        cls=torch.argmax(preds, dim=1),
        fname=self.save_dir / f"val_batch{ni}_pred.jpg",
        names=self.names,
        on_plot=self.on_plot,
    )  # pred

plot_val_samples 

plot_val_samples(batch, ni)

Plot validation image samples with their ground truth labels.

Source code in ultralytics/models/yolo/classify/val.py 
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def plot_val_samples(self, batch, ni):
    """Plot validation image samples with their ground truth labels."""
    plot_images(
        images=batch["img"],
        batch_idx=torch.arange(len(batch["img"])),
        cls=batch["cls"].view(-1),  # warning: use .view(), not .squeeze() for Classify models
        fname=self.save_dir / f"val_batch{ni}_labels.jpg",
        names=self.names,
        on_plot=self.on_plot,
    )

postprocess 

postprocess(preds)

Extract the primary prediction from model output if it's in a list or tuple format.

Source code in ultralytics/models/yolo/classify/val.py 
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def postprocess(self, preds):
    """Extract the primary prediction from model output if it's in a list or tuple format."""
    return preds[0] if isinstance(preds, (list, tuple)) else preds

preprocess 

preprocess(batch)

Preprocess input batch by moving data to device and converting to appropriate dtype.

Source code in ultralytics/models/yolo/classify/val.py 
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def preprocess(self, batch):
    """Preprocess input batch by moving data to device and converting to appropriate dtype."""
    batch["img"] = batch["img"].to(self.device, non_blocking=True)
    batch["img"] = batch["img"].half() if self.args.half else batch["img"].float()
    batch["cls"] = batch["cls"].to(self.device)
    return batch

print_results 

print_results()

Print evaluation metrics for the classification model.

Source code in ultralytics/models/yolo/classify/val.py 
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def print_results(self):
    """Print evaluation metrics for the classification model."""
    pf = "%22s" + "%11.3g" * len(self.metrics.keys)  # print format
    LOGGER.info(pf % ("all", self.metrics.top1, self.metrics.top5))

update_metrics 

update_metrics(preds, batch)

Update running metrics with model predictions and batch targets.

Source code in ultralytics/models/yolo/classify/val.py 
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def update_metrics(self, preds, batch):
    """Update running metrics with model predictions and batch targets."""
    n5 = min(len(self.names), 5)
    self.pred.append(preds.argsort(1, descending=True)[:, :n5].type(torch.int32).cpu())
    self.targets.append(batch["cls"].type(torch.int32).cpu())



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