README.md

---
license: mit
configs:
- config_name: default
  data_files:
  - split: train
    path: tars/train/*.tar
  - split: fine_tune
    path: tars/fine_tune/*.tar
language:
- en
---

# 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](https://huggingface.co/datasets/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](https://arxiv.org/pdf/2503.17074).

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

---

## 1. Downloading the Dataset Locally

You can download the dataset locally using either **Git LFS** or the [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub) Python library.

### Using Git LFS

Clone the repository (ensure [Git LFS](https://git-lfs.github.com/) is installed):

```bash
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:

```python
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](https://github.com/webdataset/webdataset). For example, to load the training split:

```python
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:

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

---

## 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:

```python
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.)

---

## Additional Considerations

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