# Ultralytics YOLO 🚀, GPL-3.0 license from ultralytics.yolo.data import build_classification_dataloader from ultralytics.yolo.engine.validator import BaseValidator from ultralytics.yolo.utils import DEFAULT_CFG from ultralytics.yolo.utils.metrics import ClassifyMetrics class ClassificationValidator(BaseValidator): def __init__(self, dataloader=None, save_dir=None, pbar=None, logger=None, args=None): super().__init__(dataloader, save_dir, pbar, logger, args) self.args.task = 'classify' self.metrics = ClassifyMetrics() def get_desc(self): return ('%22s' + '%11s' * 2) % ('classes', 'top1_acc', 'top5_acc') def init_metrics(self, model): self.pred = [] self.targets = [] def preprocess(self, batch): 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 update_metrics(self, preds, batch): self.pred.append(preds.argsort(1, descending=True)[:, :5]) self.targets.append(batch["cls"]) def get_stats(self): self.metrics.process(self.targets, self.pred) return self.metrics.results_dict def get_dataloader(self, dataset_path, batch_size): return build_classification_dataloader(path=dataset_path, imgsz=self.args.imgsz, batch_size=batch_size, workers=self.args.workers) def print_results(self): pf = '%22s' + '%11.3g' * len(self.metrics.keys) # print format self.logger.info(pf % ("all", self.metrics.top1, self.metrics.top5)) def val(cfg=DEFAULT_CFG): cfg.model = cfg.model or "yolov8n-cls.pt" # or "resnet18" cfg.data = cfg.data or "imagenette160" validator = ClassificationValidator(args=cfg) validator(model=cfg.model) if __name__ == "__main__": val()