Reference for 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)

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

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)

def get_desc(self):
    """Returns a formatted string summarizing classification metrics."""
    return ("%22s" + "%11s" * 2) % ("classes", "top1_acc", "top5_acc")

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

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 = []

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

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,
    )

def postprocess(self, preds):
    """Preprocesses the classification predictions."""
    return preds[0] if isinstance(preds, (list, tuple)) else preds

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

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))

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())