🤗 Datasets is made to be very simple to use - the API is centered around a single function, datasets.load_dataset(dataset_name, **kwargs), that instantiates a dataset. This library can be used for text/image/audio/etc. datasets. Here is an example to load a text dataset: Here is...
image datasets & model for text/book super-resolution - GitHub - tv0001/SourceBook-Dataset: image datasets & model for text/book super-resolution
Multi-scale dual-modal generative adversarial networks for text-to-image synthesis Generating images from text descriptions is a challenging task due to the natural gap between the textual and visual modalities. Despite the promising resu... B Jiang,Y Huang,W Huang,... - 《Multimedia Tools & ...
dataset = dataset.map(lambda image, label: (preprocess_for_train(image, img_size, img_size, None), label)) dataset = dataset.shuffle(shuffle_buffer).batch(batch_size) NUM_EPOCHS dataset = dataset.repeat(NUM_EPOCHS) # tf.train.match_filenames_once方法得到的结果与placeholder的机制类似, 也需...
Should only be specified for update, for which it should match existing entity or can be * for unconditional update. context - The context to associate with this operation. Returns: dataset resource type along with Response<T>.delete public abstract void delete(String resourceGroupName, String...
There is a special case where this text file contains a true affine transformation. If using theSaveoption on a raster dataset that already contains map coordinates, a text file with the x extension is written. For example, if georeferencing is performed on a TIFF image that already contains ...
21# And convert the Dataset to NumPy arrays if you'd like 22for example in tfds.as_numpy(train_dataset): 23 image, label = example['image'], example['label'] 24 assert isinstance(image, np.array) 你也可以用tfds.load执行一系列的批量示例、转换操作,然后再调用。
utils.data import DataLoader from torchvision import datasets, transforms # Write transform for image data_transform = transforms.Compose([ # 整图像大小(从大约 512x512 到 64x64) transforms.Resize(size=(64, 64)), # 水平方向上随机翻转图像 transforms.RandomHorizontalFlip(p=0.5), # p = ...
repo_idor{}_maybe_add_torch_iterable_dataset_parent_class(self.__class__)# Usage Example:iterable_dataset=load_dataset("food101",split="train",streaming=True)forexampleiniterable_dataset:print(example)breakdataset=load_dataset("rotten_tomatoes",split="train")iterable_dataset=dataset.to_iterable_...
Deep learning in medical imaging has the potential to minimize the risk of diagnostic errors, reduce radiologist workload, and accelerate diagnosis. Training such deep learning models requires large and accurate datasets, with annotations for all trainin