Caption3o-XL-v4


# **Caption3o-XL-v4**

**Caption3o-XL-v4** is a large-scale, high-quality image-caption dataset designed for training and evaluating image-to-text models. Derived from [prithivMLmods/blip3o-caption-mini-arrow](https://huggingface.co/datasets/prithivMLmods/blip3o-caption-mini-arrow) and additional curated sources, this optimized version emphasizes long-form captions and covers a wide range of real-world and artistic scenes.

## Dataset Summary

* **Format**: Parquet
* **Image resolution**: 512x512
* **Languages**: English
* **Modality**: Image-to-Text
* **License**: Apache-2.0
* **Split**: `train` (\~ 52,800 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 a serene cemetery with neatly arranged gravestones and headstones, set against a backdrop of lush green grass. The scene is framed by tall trees on either side, their leaves providing dappled shade over the area...
```

## Use Cases

1. Pretraining or finetuning vision-language models (e.g., BLIP, Flamingo, SigLIP)
2. Evaluating long-form image captioning capabilities
3. 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-XL-v4", split="train")
```

## Citation

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

And reference this curated derivative:

> **Caption3o-XL-v4 by prithivMLmods**

![22.png]("https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/AbNlLWZMg-XbMHWZgCMTe.png")

# **Caption3o-XL-v4**

**Caption3o-XL-v4** 是一款专为图像-文本（Image-to-Text）模型的训练与评估打造的大规模高质量图像-字幕数据集。其衍生自 [prithivMLmods/blip3o-caption-mini-arrow]("https://huggingface.co/datasets/prithivMLmods/blip3o-caption-mini-arrow") 及其他精选数据源，该优化版本着重支持长格式字幕创作，涵盖广泛的现实场景与艺术场景。

## 数据集概览
* **格式**：Parquet
* **图像分辨率**：512×512
* **语言**：英语
* **模态**：图像-文本（Image-to-Text）
* **许可协议**：Apache-2.0
* **数据集拆分**：训练集（`train`，约52,800条数据）

每张图像均配有详尽的描述性字幕，旨在助力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 RGB图像
* `caption`：长格式英文文本（平均长度约500字符）

示例如下：
text
The image depicts a serene cemetery with neatly arranged gravestones and headstones, set against a backdrop of lush green grass. The scene is framed by tall trees on either side, their leaves providing dappled shade over the area...

## 应用场景
1. 视觉语言模型（如BLIP、Flamingo、SigLIP）的预训练与微调
2. 长格式图像字幕生成能力的评估
3. 优化视觉叙事、场景理解与艺术诠释相关的数据集

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

dataset = load_dataset("prithivMLmods/Caption3o-XL-v4", split="train")

## 引用说明
若使用本数据集，请引用原始数据集，并标注此衍生整理版本：
> **prithivMLmods 整理的 Caption3o-XL-v4**