---
title: zerochan-webp-4Mpixel
canonical_url: "https://www.modelscope.ai/datasets/deepghs/zerochan-webp-4Mpixel"
md_url: "https://www.modelscope.ai/datasets/deepghs/zerochan-webp-4Mpixel.md"
repository: deepghs/zerochan-webp-4Mpixel
last_updated: 2026-08-06
license: other
storage_size: "900 GB"
downloads: 734
stars: 0
---

# zerochan-webp-4Mpixel

> zerochan-webp-4Mpixel - An open-source dataset by deepghs on ModelScope. Zerochan Newest 4M Re-encoded Dataset

deepghs/zerochan-webp-4Mpixel is a dataset on ModelScope. totalling 900 GB. licensed under other.

- **Repository**: deepghs/zerochan-webp-4Mpixel
- **License**: other
- **Storage size**: 900 GB
- **Downloads**: 734
- **Stars**: 0
- **Last updated**: 2026-08-06

Source: https://www.modelscope.ai/datasets/deepghs/zerochan-webp-4Mpixel

---

# Zerochan Newest 4M Re-encoded Dataset

This is the re-encoded dataset of [deepghs/zerochan_full](https://huggingface.co/datasets/deepghs/zerochan_full). And all the resized images are maintained here.

There are 4106075 images in total. The maximum ID of these images is 4588400. Last updated at `2025-09-22 02:45:45 JST`.

# How to Painlessly Use This

Use [cheesechaser](https://github.com/deepghs/cheesechaser) to quickly get images from this repository.

Before using this code, you have to **grant the access from this gated repository**. And then **set your personal HuggingFace token into `HF_TOKEN` environment variable** to give the code authorization for this repository.

```python
from cheesechaser.datapool import ZerochanWebpDataPool

pool = ZerochanWebpDataPool()
pool.batch_download_to_directory(
    # download images #3000000-3000100, any ranges or id lists are okay
    resource_ids=range(3000000, 3000100),

    # save to directory /data/zerochan_webp
    dst_dir='/data/zerochan_webp',
)
```