Skip to content

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 ūüõ†ÔłŹ. Thank you ūüôŹ!


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.

Notes
  • Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'.
Example
from ultralytics.models.yolo.classify import ClassificationValidator

args = dict(model='yolov8n-cls.pt', data='imagenet10')
validator = ClassificationValidator(args=args)
validator()
Source code in ultralytics/models/yolo/classify/val.py
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
    """Initializes ClassificationValidator instance with args, dataloader, save_dir, and progress bar."""
    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)

Creates and returns a ClassificationDataset instance using given image path and preprocessing parameters.

Source code in ultralytics/models/yolo/classify/val.py
def build_dataset(self, img_path):
    """Creates and returns a ClassificationDataset instance using given image path and preprocessing parameters."""
    return ClassificationDataset(root=img_path, args=self.args, augment=False, prefix=self.args.split)

finalize_metrics

finalize_metrics(*args, **kwargs)

Finalizes metrics of the model such as confusion_matrix and speed.

Source code in ultralytics/models/yolo/classify/val.py
def finalize_metrics(self, *args, **kwargs):
    """Finalizes metrics of the model such as confusion_matrix and 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)

Builds and returns a data loader for classification tasks with given parameters.

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

get_desc

get_desc()

Returns a formatted string summarizing classification metrics.

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

get_stats

get_stats()

Returns a dictionary of metrics obtained by processing targets and predictions.

Source code in ultralytics/models/yolo/classify/val.py
def get_stats(self):
    """Returns a dictionary of metrics obtained 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 top-1 and top-5 accuracy.

Source code in ultralytics/models/yolo/classify/val.py
def init_metrics(self, model):
    """Initialize confusion matrix, class names, and top-1 and top-5 accuracy."""
    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)

Plots predicted bounding boxes on input images and saves the result.

Source code in ultralytics/models/yolo/classify/val.py
def plot_predictions(self, batch, preds, ni):
    """Plots predicted bounding boxes on input images and saves the result."""
    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.

Source code in ultralytics/models/yolo/classify/val.py
def plot_val_samples(self, batch, ni):
    """Plot validation image samples."""
    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,
    )

preprocess

preprocess(batch)

Preprocesses input batch and returns it.

Source code in ultralytics/models/yolo/classify/val.py
def preprocess(self, batch):
    """Preprocesses input batch and returns it."""
    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()

Prints evaluation metrics for YOLO object detection model.

Source code in ultralytics/models/yolo/classify/val.py
def print_results(self):
    """Prints evaluation metrics for YOLO object detection 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)

Updates running metrics with model predictions and batch targets.

Source code in ultralytics/models/yolo/classify/val.py
def update_metrics(self, preds, batch):
    """Updates 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 2023-11-12, Updated 2024-07-21
Authors: glenn-jocher (6), Burhan-Q (1)