Accessing the font-square-v2 Dataset on Hugging Face

The font-square-v2 dataset is hosted on Hugging Face at blowing-up-groundhogs/font-square-v2. It is stored in WebDataset format, with tar files organized as follows:

• tars/train/: Contains {000..499}.tar shards for the main training split.
• tars/fine_tune/: Contains {000..049}.tar shards for fine-tuning.

Each tar file contains multiple samples, where each sample includes:

• An RGB image (.rgb.png)
• A black-and-white image (.bw.png)
• A JSON file (.json) with metadata (e.g. text and writer ID)

For details on how the synthetic dataset was generated, please refer to our paper: Synthetic Dataset Generation.

You can access the dataset either by downloading it locally or by streaming it directly over HTTP.

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1. Downloading the Dataset Locally

You can download the dataset locally using either Git LFS or the huggingface_hub Python library.

Using Git LFS

Clone the repository (ensure Git LFS is installed):

git lfs clone https://huggingface.co/datasets/blowing-up-groundhogs/font-square-v2

This creates a local directory font-square-v2 containing the tars/ folder with the subdirectories train/ and fine_tune/.

Using the huggingface_hub Python Library

Alternatively, download a snapshot of the dataset:

from huggingface_hub import snapshot_download

# Download the repository; the local path is returned
local_dir = snapshot_download(repo_id="blowing-up-groundhogs/font-square-v2", repo_type="dataset")
print("Dataset downloaded to:", local_dir)

After downloading, the tar shards are located in:

• local_dir/tars/train/{000..499}.tar
• local_dir/tars/fine_tune/{000..049}.tar

Using WebDataset with the Local Files

Once downloaded, you can load the dataset using WebDataset. For example, to load the training split:

import webdataset as wds
import os

local_dir = "path/to/font-square-v2"  # Update as needed

# Load training shards
train_pattern = os.path.join(local_dir, "tars", "train", "{000..499}.tar")
train_dataset = wds.WebDataset(train_pattern).decode("pil")

for sample in train_dataset:
    rgb_image = sample["rgb.png"]  # PIL image
    bw_image = sample["bw.png"]    # PIL image
    metadata = sample["json"]

    print("Training sample metadata:", metadata)
    break

And similarly for the fine-tune split:

fine_tune_pattern = os.path.join(local_dir, "tars", "fine_tune", "{000..049}.tar")
fine_tune_dataset = wds.WebDataset(fine_tune_pattern).decode("pil")

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2. Streaming the Dataset Directly Over HTTP

If you prefer not to download the shards, you can stream them directly from Hugging Face using the CDN (provided the tar files are public). For example:

import webdataset as wds

url_pattern = (
    "https://huggingface.co/datasets/blowing-up-groundhogs/font-square-v2/resolve/main"
    "/tars/train/{000000..000499}.tar"
)

dataset = wds.WebDataset(url_pattern).decode("pil")

for sample in dataset:
    rgb_image = sample["rgb.png"]
    bw_image = sample["bw.png"]
    metadata = sample["json"]

    print("Sample metadata:", metadata)
    break

(Adjust the shard range accordingly for the fine-tune split.)

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Additional Considerations

• Decoding:
  The .decode("pil") method in WebDataset converts image bytes into PIL images. To use PyTorch tensors, add a transform step:

  import torchvision.transforms as transforms
  transform = transforms.ToTensor()
  
  dataset = (
      wds.WebDataset(train_pattern)
      .decode("pil")
      .map(lambda sample: {
          "rgb": transform(sample["rgb.png"]),
          "bw": transform(sample["bw.png"]),
          "metadata": sample["json"]
      })
  )
  

• Shard Naming:
  Ensure your WebDataset pattern matches the following structure:

  tars/
  ├── train/
  │   └── {000..499}.tar
  └── fine_tune/
      └── {000..049}.tar
  

By following these instructions, you can easily integrate the font-square-v2 dataset into your project for training and fine-tuning.