metadata

license: mit

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. The dataset is stored in WebDataset format, with all tar files located in the tars/ folder of the repository. Each tar file contains multiple samples; each sample includes:

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

You can access the dataset in one of two ways: 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 Python library.

Using Git LFS

Clone the repository (make sure Git LFS is installed):

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

This will create a local directory named font-square-v2 that contains the tars/ folder with all the tar shards.

Using the huggingface_hub Python Library

Alternatively, you can download a snapshot of the dataset in your Python code:

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 available in the tars/ subdirectory of local_dir.

Using WebDataset with the Local Files

Once the dataset is downloaded locally, you can load it with WebDataset. For example:

import webdataset as wds
import os
from pathlib import Path

# Assuming the dataset was downloaded to `local_dir` and tar shards are in the 'tars' folder
local_dir = "path/to/font-square-v2"  # Update this path if necessary
tar_pattern = os.path.join(local_dir, "tars", "{000000..000499}.tar")  # Adjust range to match your tar shard naming

# Create a WebDataset
dataset = wds.WebDataset(tar_pattern).decode("pil")

# Example: Iterate over a few samples
for sample in dataset:
    # Access sample items
    rgb_image = sample["rgb.png"]  # RGB image (PIL image)
    bw_image = sample["bw.png"]    # BW image (PIL image)
    metadata = sample["json"]

    print("Sample metadata:", metadata)
    
    # Process the images as needed...
    break

2. Streaming the Dataset Directly Over HTTP

If you prefer to stream the dataset directly from Hugging Face (without downloading the entire dataset), you can use the HTTP URLs provided by the Hugging Face CDN. Make sure that your tar files are public and accessible.

For example, if the tar shards are available at:

https://huggingface.co/datasets/blowing-up-groundhogs/font-square-v2/resolve/main/tars/000000.tar
https://huggingface.co/datasets/blowing-up-groundhogs/font-square-v2/resolve/main/tars/000001.tar
...

you can set up your WebDataset as follows:

import webdataset as wds

# Define the URL pattern to stream the tar shards directly from Hugging Face
url_pattern = "https://huggingface.co/datasets/blowing-up-groundhogs/font-square-v2/resolve/main/tars/{000000..000499}.tar"
# Adjust the shard range (here 000000 to 000010) to cover all your tar shards.

# Create a WebDataset that streams data
dataset = wds.WebDataset(url_pattern).decode("pil")

# Iterate over a few samples from the streamed dataset
for sample in dataset:
    rgb_image = sample["rgb.png"]
    bw_image = sample["bw.png"]
    metadata = sample["json"]

    print("Sample metadata:", metadata)
    
    # Process sample as needed...
    break

Note: Streaming performance depends on your network connection and the Hugging Face CDN. If you experience any slowdowns, consider downloading the dataset locally instead.

Additional Considerations

Decoding:
The .decode("pil") method in the WebDataset pipeline converts image bytes into PIL images. If you prefer PyTorch tensors, you can add a transformation:

import torchvision.transforms as transforms
transform = transforms.ToTensor()

dataset = (
    wds.WebDataset(url_pattern)
    .decode("pil")
    .map(lambda sample: {
        "rgb": transform(sample["rgb.png"]),
        "bw": transform(sample["bw.png"]),
        "metadata": sample["json"],
    })
)

Shard Naming:
Ensure that the naming convention in your tars/ folder matches the URL pattern used above. Adjust the pattern {000000..000499} accordingly if your tar files have a different naming scheme or if there are more shards.

By following these instructions, you can easily load and work with the font-square-v2 dataset from Hugging Face in your Python projects using WebDataset.

Accessing the font-square-v2 Dataset on Hugging Face