Caption3o-Opt-v2

# **Caption3o-Opt-v2**

**Caption3o-Opt-v2** is a high-quality, compact image-caption dataset designed for training and evaluating image-to-text models. Derived from the larger [BLIP3o/BLIP3o-Pretrain-Long-Caption](https://huggingface.co/datasets/BLIP3o/BLIP3o-Pretrain-Long-Caption), this optimized subset emphasizes long-form captions and covers a wide range of real-world and artistic scenes.

## Dataset Summary

* **Size**: 10,277 image-caption pairs
* **Format**: Parquet
* **Image resolution**: 512x512
* **Languages**: English
* **Modality**: Image-to-Text
* **License**: Apache-2.0
* **Split**: `train` (10.3k rows)

Each image is paired with a detailed, descriptive caption generated to support long-context understanding and fine-grained reasoning in vision-language tasks.

## Features

* `image`: 512x512 RGB image
* `caption`: Long-form English text (average length \~500 characters)

Example:

```text
The image depicts the upper section of a classical-style building, featuring a decorative frieze with relief sculptures...
```

## Use Cases

* Pretraining or finetuning vision-language models (e.g., BLIP, Flamingo, SigLIP)
* Evaluating long-form image captioning capabilities
* Enhancing datasets for visual storytelling, scene understanding, and artistic interpretation

## How to Use

You can load the dataset using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset("prithivMLmods/Caption3o-Opt-v2", split="train")
```

## Citation

If you use this dataset, please cite the original dataset:

> **BLIP3o/BLIP3o-Pretrain-Long-Caption**
> [https://huggingface.co/datasets/BLIP3o/BLIP3o-Pretrain-Long-Caption](https://huggingface.co/datasets/BLIP3o/BLIP3o-Pretrain-Long-Caption)

And reference this curated derivative:

> **Caption3o-Opt-v2 by prithivMLmods**

# **Caption3o-Opt-v2**

**Caption3o-Opt-v2** 是一款高质量、轻量化的图像-文本数据集，专为图像转文本模型的训练与评估打造。其源自大规模数据集[BLIP3o/BLIP3o-Pretrain-Long-Caption](https://huggingface.co/datasets/BLIP3o/BLIP3o-Pretrain-Long-Caption)，经过优化的子集聚焦于长格式字幕，覆盖了广泛的现实场景与艺术创作场景。

## 数据集概览
* **规模**：10277组图像-文本配对样本
* **存储格式**：Parquet
* **图像分辨率**：512×512
* **语言**：英语
* **模态**：图像到文本
* **许可协议**：Apache-2.0
* **数据划分**：训练集（`train`，共约10.3k条数据）

每组图像均搭配一段详细的描述性字幕，旨在支撑视觉语言任务中的长上下文理解与细粒度推理。

## 数据集字段
* `image`：512×512的RGB图像
* `caption`：长格式英文文本（平均长度约500个字符）

示例：
text
本图像展示了一座古典风格建筑的上部区域，带有装饰性的雕带及浮雕雕塑……

## 应用场景
* 视觉语言模型（Vision-Language Models，如BLIP、Flamingo、SigLIP）的预训练与微调
* 长格式图像字幕生成能力的评估
* 优化用于视觉叙事、场景理解与艺术诠释的数据集

## 使用方法
你可以通过Hugging Face的`datasets`库加载该数据集：
python
from datasets import load_dataset

dataset = load_dataset("prithivMLmods/Caption3o-Opt-v2", split="train")

## 引用说明
若你使用本数据集，请引用其原始数据集：
> **BLIP3o/BLIP3o-Pretrain-Long-Caption**
> [https://huggingface.co/datasets/BLIP3o/BLIP3o-Pretrain-Long-Caption](https://huggingface.co/datasets/BLIP3o/BLIP3o-Pretrain-Long-Caption)

同时请标注此经过精选的衍生数据集：
> **prithivMLmods 制作的 Caption3o-Opt-v2**