metadata
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:
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}