| def train_transforms(example_batch): | |
| images = [aug_transforms(x.convert("RGB")) for x in example_batch["image"]] | |
| labels = [x for x in example_batch["annotation"]] | |
| inputs = image_processor(images, labels) | |
| return inputs | |
| def val_transforms(example_batch): | |
| images = [transforms(x.convert("RGB")) for x in example_batch["image"]] | |
| labels = [x for x in example_batch["annotation"]] | |
| inputs = image_processor(images, labels) | |
| return inputs | |
| To apply the preprocessing transformations over the entire dataset, use the 🤗 Datasets [~datasets.Dataset.set_transform] function. |