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---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: test
num_bytes: 1024849819
num_examples: 10000
download_size: 1018358664
dataset_size: 1024849819
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
license: mit
language:
- en
tags:
- art
size_categories:
- 1K<n<10K
task_categories:
- visual-question-answering
- question-answering
- text-to-image
---
## Dataset Description
The **Products-10k BLIP CAPTIONS** dataset consists of 10000 images of various products along with their automatically generated captions. The captions are generated using the BLIP (Bootstrapping Language-Image Pre-training) model. This dataset aims to aid in tasks related to image captioning, visual recognition, and product classification.
## Dataset Summary
- **Dataset Name**: Products-10k
- **Generated Captions Model**: Salesforce/blip-image-captioning-large
- **Number of Images**: 10,000
- **Image Formats**: JPEG, PNG
- **Captioning Prompt**: "Photography of"
- **Source**: The images are sourced from a variety of product categories.
## Dataset Structure
The dataset is structured as follows:
- **image**: Contains the product images in RGB format.
- **text**: Contains the generated captions for each product image.
## Usage
You can load and use this dataset with the Hugging Face `datasets` library as follows:
```python
from datasets import load_dataset
dataset = load_dataset("VikramSingh178/Products-10k-BLIP-captions", split="test")
# Display an example
example = dataset[0]
image = example["image"]
caption = example["text"]
image.show()
print("Caption:", caption)
```
```
author = {Yalong Bai, Yuxiang Chen, Wei Yu, Linfang Wang, Wei Zhang},
title = {Products-10K: A Large-scale Product Recognition Dataset},
journal = {arXiv},
year = {2024},
url = {https://arxiv.org/abs/2008.10545}
``` |