| | --- |
| | 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-* |
| | license: cc |
| | size_categories: |
| | - 100K<n<1M |
| | --- |
| | |
| |
|
| | # 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. |
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| | 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! |
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| | Thank you LeCun & Cortes for making this dataset available. |
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