| language: | |
| - en | |
| license: cc | |
| size_categories: | |
| - 100K<n<1M | |
| dataset_info: | |
| features: | |
| - name: image | |
| dtype: image | |
| - name: text | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 39858266 | |
| num_examples: 140000 | |
| download_size: 37136812 | |
| dataset_size: 39858266 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| # MNIST for Diffusion | |
| Training a diffusion model from scratch is pretty cool, why not do so with the canonical "hello world" dataset of computer vision? This dataset matches the sample dataset from [this text_to_image.py diffusion tutorial](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image). Specifying `ckg/mnist-for-diffusion` ought get you off to the races. | |
| This dataset contains two copies of the original MNIST train & test sets. The first half of the dataset contains MNIST images with the string-ified class id (i.e: "1") and the second half has the class id mapped to a natural language name (i.e: "one"). This little data augmentation doubles the number of samples and should result in interesting behavior if you train a U-Net from scratch whilst using a frozen, pre-trained text-encoder! | |
| Thank you LeCun & Cortes for making this dataset available. | |