blip3o-caption-mini-arrow

# **blip3o-caption-mini-arrow**

**blip3o-caption-mini-arrow** is a high-quality, curated image-caption dataset derived and optimized from the original [BLIP3o/BLIP3o-Pretrain-Long-Caption](https://huggingface.co/datasets/BLIP3o/BLIP3o-Pretrain-Long-Caption). This dataset is specifically filtered and processed for tasks involving long-form image captioning and vision-language understanding.

## Overview

* **Total Samples**: 91,600
* **Modality**: Image ↔ Text
* **Format**: Arrow (auto-converted to Parquet)
* **License**: Apache 2.0
* **Language**: English
* **Size**: \~4.5 GB

## Dataset Structure

| Field | Type | Description |
| ------- | ------ | ----------------------------------------------- |
| image | image | Input image (stored in binary format) |
| caption | string | Descriptive caption for the image (long format) |

## Quick start with Datasets🤗

```
pip install datasets
```

```py
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("prithivMLmods/blip3o-caption-mini-arrow", split="train")

# View a sample
print(dataset[0])

```

## Example Entries

1. **Image**: A religious statue
**Caption**: *The image depicts a religious figure adorned in elaborate, ornate attire, likely a statue or icon of a saint or Virgin Mary...*

2. **Image**: A historic building with a clock tower
**Caption**: *The image captures a grand, historic building under a clear blue sky. The structure features ornate architectural details...*

3. **Image**: A vibrant South Asian temple
**Caption**: *The image depicts the entrance of a vibrant and ornate temple, likely of South Asian origin...*

## Use Cases

This dataset is ideal for:

* Training image captioning models
* Evaluating visual grounding and long-text generation
* Multi-modal representation learning
* Fine-tuning vision-language models like BLIP, Flamingo, or IDEFICS

## Citation

If you use this dataset, please consider citing the original BLIP3o dataset and linking to this derivative version.

# **blip3o-caption-mini-arrow**

**blip3o-caption-mini-arrow** 是一套高质量经精选的图像-字幕（image-caption）数据集，源自原始[BLIP3o/BLIP3o-Pretrain-Long-Caption](https://huggingface.co/datasets/BLIP3o/BLIP3o-Pretrain-Long-Caption)并针对相关任务做了优化。本数据集经过专门筛选与处理，适配长文本图像字幕生成与视觉语言理解任务。

## 概览
* **总样本量**：91,600
* **模态**：图像 ↔ 文本
* **格式**：Arrow（可自动转换为Parquet格式）
* **许可协议**：Apache 2.0
* **语言**：英语
* **数据体量**：约4.5 GB

## 数据集结构
| 字段名 | 数据类型 | 描述 |
| ------- | ------ | ----------------------------------------------- |
| image | 图像类型 | 输入图像（以二进制格式存储） |
| caption | 字符串 | 针对该图像的描述性字幕（长文本格式） |

## 快速使用（基于🤗 Datasets 库）

pip install datasets

py
from datasets import load_dataset

# 加载数据集
dataset = load_dataset("prithivMLmods/blip3o-caption-mini-arrow", split="train")

# 查看单条样本
print(dataset[0])

## 示例条目
1. **图像**：宗教雕像
**字幕**：*本图像呈现了一尊身着繁复华丽服饰的宗教造像，大概率为某位圣徒或圣母玛利亚的雕像或圣像……*
2. **图像**：带钟楼的历史建筑
**字幕**：*本图像定格了晴朗蓝天下一座宏伟的历史建筑。该结构拥有精致典雅的建筑细节……*
3. **图像**：色彩鲜活的南亚寺庙
**字幕**：*本图像展现了一座色彩明艳、装饰华丽的寺庙入口，大概率源自南亚地区……*

## 应用场景
本数据集适用于以下任务：
* 训练图像字幕生成模型
* 评估视觉锚定与长文本生成能力
* 多模态表征学习
* 针对BLIP、Flamingo或IDEFICS等视觉语言模型进行微调

## 引用说明
若您使用本数据集，请引用原始BLIP3o数据集，并链接至该衍生版本。