Update README.md

README.md

@@ -7,13 +7,25 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 39858266.0
+    num_bytes: 39858266
     num_examples: 140000
   download_size: 37136812
-  dataset_size: 39858266.0
+  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.
+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.