SpudPix

数据集文件元信息以及数据文件，请浏览“数据集文件”页面获取。

当前数据集卡片使用的是默认模版，数据集的贡献者未提供更加详细的数据集介绍，但是您可以通过如下GIT Clone命令，或者ModelScope SDK来下载数据集

#### 下载方法
:modelscope-code[]{type="sdk"}
:modelscope-code[]{type="git"}

# SpudPix

## 数据集描述

### 数据集摘要

本数据集包含约 **14 万** 条从公开网络采集并经过筛选的多模态数据。每条数据由 **图像文件名** 和 **对应的图像内容** 组成。该数据集旨在为计算机视觉、多模态学习等研究领域提供高质量的原始素材。所有数据均经过初步筛选，以确保基础的可用性。

### 支持的任务和排行榜

该数据集可用于多种下游任务，包括但不限于：
* **图像分类 (Image Classification)**: 训练模型以识别图像中的物体或场景。
* **图文生成 (Text-to-Image Generation)**: 作为训练扩散模型或其他生成模型的基础图像数据。
* **图像聚类 (Image Clustering)**: 根据视觉特征对图像进行无监督分组。
* **自监督学习 (Self-Supervised Learning)**: 利用大规模无标签图像进行预训练。

### 语言

数据集本身主要由图像构成，不直接包含文本。文件名主要使用 `[例如：英文、数字组合]`。

## 如何使用

### 安装依赖
为了加载和处理图像数据，请确保你已安装 `datasets` 和 `Pillow` 库。

```bash
pip install datasets pillow
```

你可以使用 datasets 库轻松加载数据集。推荐使用流式加载 (streaming=True)，这样可以避免一次性将全部数据加载到内存中，特别适合处理大规模数据集。
为了加载和处理图像数据，请确保你已安装 `datasets` 和 `Pillow` 库。

Dataset file metadata and data files are available on the "Dataset Files" page.

This dataset card uses the default template, and the dataset contributors have not provided a more detailed introduction. You can download the dataset via the following Git Clone command or ModelScope SDK:

#### Download Method
:modelscope-code[]{type="sdk"}
:modelscope-code[]{type="git"}

# SpudPix

## Dataset Description

### Dataset Summary
This dataset contains approximately 140,000 pieces of filtered multimodal data collected from public networks. Each entry consists of an **image filename** and its corresponding **image content**. This dataset aims to provide high-quality raw materials for research fields such as computer vision and multimodal learning. All data has undergone preliminary screening to ensure basic usability.

### Supported Tasks and Leaderboards
This dataset can be used for a variety of downstream tasks, including but not limited to:
* **Image Classification**: Train models to recognize objects or scenes within images.
* **Text-to-Image Generation**: Serve as foundational image data for training diffusion models or other generative models.
* **Image Clustering**: Perform unsupervised grouping of images based on visual features.
* **Self-Supervised Learning**: Conduct pre-training using large-scale unlabeled image data.

### Languages
The dataset itself is primarily composed of images and does not directly contain text. Filenames mainly use `[for example: combinations of English letters and numbers]`.

## How to Use

### Install Dependencies
To load and process image data, ensure you have installed the `datasets` and `Pillow` libraries.
bash
pip install datasets pillow

You can easily load the dataset using the `datasets` library. Streaming loading (streaming=True) is recommended, as it avoids loading all data into memory at once, which is particularly suitable for handling large-scale datasets.
To load and process image data, ensure you have installed the `datasets` and `Pillow` libraries.