datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Military-Aircraft-Recognition-datasetThis is a remote sensing image Military Aircraft Recognition dataset that include 3842 images, 20 types, and 22341 instances annotated with horizontal bounding boxes and oriented bounding boxes.
fashion-recommendation-images
High-Resolution Fashion Product Images
This dataset is a highly optimized, high-resolution subset of the popular Fashion Product Images Dataset originally hosted on Kaggle.
It contains thousands of unique e-commerce fashion products, combining high-resolution product images with multiple descriptive label attributes.
All low-resolution thumbnails and anomalies have been aggressively filtered out. Every image in this dataset has a minimum resolution of 640px on its shortest… See the full description on the dataset page: https://huggingface.co/datasets/GangHitman/fashion-recommendation-images.multi-label-food-recognition
Multi-Label Food Recognition Dataset
This is a multi-label food recognition dataset generated from single-class food images.
Each image contains 2-5 different food items composited together using natural composition methods.
Dataset Details
Total Images: 13,000
Training Images: 10,400 (80%)
Validation Images: 2,600 (20%)
Number of Classes: 90
Labels per Image: 2-5 labels
Image Format: RGB, 512x512 pixels
File Format: Parquet
Dataset Structure
Each sample… See the full description on the dataset page: https://huggingface.co/datasets/ibrahimdaud/multi-label-food-recognition.Recap-DataComp-1B-FoodOrDrink
Recap-DataComp-1B: Food or Drink
A filtered subset of Recap-DataComp-1B containing 106,230,157 rows classified as food/drink content, enriched with structured food/drink extraction from FoodExtract-v2.
Overview
Count
Percentage
Total rows
106,230,157
100%
Food/drink (Stage 5 label)
96,618,895
91.0%
Not food/drink (Stage 5 label)
9,611,262
9.0%
FoodExtract (re_caption): food/drink
79,519,489
74.9%
FoodExtract (re_caption): not food/drink
26,710,156… See the full description on the dataset page: https://huggingface.co/datasets/mrdbourke/Recap-DataComp-1B-FoodOrDrink.Recraft-V2_t2i_human_preference
Rapidata Recraft-V2 Preference
This T2I dataset contains over 195k human responses from over 47k individual annotators, collected in just ~1 Day using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating Recraft-V2 across three categories: preference, coherence, and alignment.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please consider liking it.… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Recraft-V2_t2i_human_preference.Recraft-v3-24-7-25_t2i_human_preference
Rapidata Recraft v3 Preference
This T2I dataset contains over ~400'000 human responses from over ~50'000 individual annotators, collected in less than 7h using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating Recraft v3 (version from 24.7.2025) across three categories: preference, coherence, and alignment.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Recraft-v3-24-7-25_t2i_human_preference.nano-receipts
🧾 Nano Receipts Dataset
A diverse collection of 2428 hyper-realistic synthetic receipt images generated using state-of-the-art text-to-image AI models.
🚀 Quick Start
from datasets import load_dataset
# Load dataset (fast parquet format!)
dataset = load_dataset("34data/nano-receipts")
# Access images
image = dataset["train"][0]["image"] # PIL Image
filename = dataset["train"][0]["filename"]
📊 Dataset Details
Total Images: 2428 receipts
Format:… See the full description on the dataset page: https://huggingface.co/datasets/34data/nano-receipts.gpt4o-receipt
GPT4o-Receipt: AI-Generated Receipt Dataset
This directory contains the AI-generated receipts from the
GPT4o-Receipt benchmark, introduced in:
GPT4o-Receipt: A Dataset and Human Study for AI-Generated Document ForensicsYan Zhang*, Simiao Ren*†, Ankit Raj, En Wei, Dennis Ng, Alex Shen, Jiayu Xue, Yuxin Zhang, Evelyn MarottaarXiv:2603.11442 · March 2026 · CC BY-NC-SA 4.0*Equal contribution. †Corresponding author: benren@scam.ai
What Is GPT4o-Receipt?
GPT4o-Receipt is… See the full description on the dataset page: https://huggingface.co/datasets/Scam-AI/gpt4o-receipt.raw-food-recognition
Merged Raw Food Recognition Dataset
Dataset Description
This dataset is a comprehensive compilation of three publicly available food recognition datasets, merged and curated for raw food recognition tasks. The dataset contains images of various raw food items including fruits, vegetables, dairy products, and beverages, intended for educational purposes and the development of image recognition models.
Purpose
This dataset is created for educational purposes only… See the full description on the dataset page: https://huggingface.co/datasets/ibrahimdaud/raw-food-recognition.povarenok_recipes_detail
povarenok_recipes_detail
Crawled detailed recipes from povarenok.ru website.
Structure
WIP
medical_records_parsing_validation_set
Medical Records Parsing Validation Set
Dataset Composition and Clinical Relevance
The Eka Medical Records Parsing Dataset empowers evaluation of AI systems designed to extract structured information from unstructured medical documents, enabling true digitisation of healthcare data while maintaining clinical accuracy.
The dataset comprise 288 carefully selected images of laboratory reports and prescriptions representing diverse formats and templates encountered in Indian… See the full description on the dataset page: https://huggingface.co/datasets/ekacare/medical_records_parsing_validation_set.Recruitment-Task-3
DeepWeeds - AI-MED AGH convenience mirror
This is a convenience mirror of the official DeepWeeds image archive and the
upstream annotations pinned to a specific commit. original/images.zip is
preserved unchanged; images are not extracted or duplicated here. models.zip
from the source authors is deliberately not mirrored.
Dataset facts
17,509 in-situ images from Queensland, Australia.
Nine classes: eight weed species plus Negative.
The authors publish five folds… See the full description on the dataset page: https://huggingface.co/datasets/AI-MED-AGH/Recruitment-Task-3.japanese-image-classification-evaluation-dataset
recruit-jp/japanese-image-classification-evaluation-dataset
Overview
Developed by: Recruit Co., Ltd.
Dataset type: Image Classification
Language(s): Japanese
LICENSE: CC-BY-4.0
More details are described in our tech blog post.
日本語CLIP学習済みモデルとその評価用データセットの公開
Dataset Details
This dataset is comprised of four image classification tasks related to concepts and things unique to Japan. Specifically, is consists of the following tasks.
jafood101: Image… See the full description on the dataset page: https://huggingface.co/datasets/recruit-jp/japanese-image-classification-evaluation-dataset.nano-receipts
🧾 Nano Receipts Dataset
A diverse collection of 2428 hyper-realistic synthetic receipt images generated using state-of-the-art text-to-image AI models.
🚀 Quick Start
from datasets import load_dataset
# Load dataset (fast parquet format!)
dataset = load_dataset("34data/nano-receipts")
# Access images
image = dataset["train"][0]["image"] # PIL Image
filename = dataset["train"][0]["filename"]
📊 Dataset Details
Total Images: 2428 receipts… See the full description on the dataset page: https://huggingface.co/datasets/samarth010/nano-receipts.Amusement-Park-Game-Facility-Recognition-Image-Dataset
Amusement Park Game Facility Recognition Image Dataset
In the retail e-commerce sector, as consumer demand for amusement park facilities increases, merchants face challenges in quickly identifying and managing various facilities. Existing image recognition technologies still lack in accuracy and speed, especially in scenarios with diverse facility combinations. This dataset aims to enhance the precision and efficiency of amusement park facility recognition, satisfying the business… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Amusement-Park-Game-Facility-Recognition-Image-Dataset.Infant-Sleep-Posture-Recognition-Image-Dataset
Infant Sleep Posture Recognition Image Dataset
Currently, there are many challenges in improving infant sleep safety, including the difficulty of assessing posture and monitoring sleep status without affecting normal infant sleep. Existing monitoring equipment often relies on clothing sensors, which may cause discomfort and excessive interference. This dataset aims to address the risk of asphyxiation caused by incorrect infant sleep posture through visual recognition technology… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Infant-Sleep-Posture-Recognition-Image-Dataset.Pansy-Recognition-Image-Dataset
Pansy Recognition Image Dataset
Currently, garden management faces the challenge of efficiently and accurately identifying flower varieties. Traditional manual identification relies on experience and is inefficient. Existing image recognition technologies still need improvement in the accuracy of specific flower types, especially in complex backgrounds. This dataset aims to address common accuracy deficiencies in pansy recognition by providing a large number of high-quality images… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Pansy-Recognition-Image-Dataset.Mold-Template-Defect-Recognition-Dataset
Mold Template Defect Recognition Dataset
In the industrial sector, mold quality inspection is crucial for ensuring product integrity but faces challenges such as inconsistent defect detection and high rates of false negatives. Existing solutions often rely on manual inspection, which is time-consuming and prone to human error, leading to inefficiencies. This dataset aims to address these challenges by providing a comprehensive collection of labeled images that enhance machine… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Mold-Template-Defect-Recognition-Dataset.Traffic-Congestion-Recognition-Dataset
Traffic Congestion Recognition Dataset
The main challenge currently faced by the transportation industry is the increasingly severe urban congestion problem, leading to low traffic efficiency and aggravated environmental pollution. Existing traffic monitoring systems often rely on static data, lacking real-time performance and flexibility. To solve this problem, this dataset aims to provide high-quality congestion recognition data to support real-time decision-making and management… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Traffic-Congestion-Recognition-Dataset.index-cards-southborough-vital-records
Southborough Town Clerk — Vital Records & Veteran Index Cards (MA)
8,661 cards from the Southborough (Massachusetts) Town Clerk office, covering:
Death Index Cards, 1850–2015 — the town clerk's running death index covering ~165 years of Southborough deaths, one card per decedent with surname/given-name/date.
Veteran Card Index + Veteran Grave Registration Card Index — companion indices to Southborough's veteran-affairs records, indexing veterans buried in town cemeteries.… See the full description on the dataset page: https://huggingface.co/datasets/biglam/index-cards-southborough-vital-records.Hair-Dryer-Nozzle-Type-Recognition-Dataset
Hair Dryer Nozzle Type Recognition Dataset
In the current retail e-commerce industry, there is a growing need for accurate product classification to enhance customer shopping experiences. However, existing solutions often struggle with classifying various hair dryer nozzle types due to high variability in design and appearance. This dataset aims to address the technical challenge of recognizing and categorizing nozzle types, which is crucial for improving product recommendations and… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Hair-Dryer-Nozzle-Type-Recognition-Dataset.Washing-Machine-Appearance-Recognition-Dataset
Washing Machine Appearance Recognition Dataset
The washing machine industry faces challenges in accurately identifying different types of washing machines, such as drum, pulsator, and mini types, which complicates inventory management and enhances the risk of misclassification. Current solutions often rely on manual classification, which is time-consuming and error-prone. This dataset aims to provide a robust framework for training machine learning models that can automate the… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Washing-Machine-Appearance-Recognition-Dataset.burmese-pyu-character-recognition
Burmese Pyu Character Recognition Dataset
English | မြန်မာဘာသာ
Overview
This Dataset is an image collection created for the purpose of computer recognition (Character Recognition) and research of the "Pyu" alphabet, an ancient script of Myanmar.
Brief Historical Background
The Pyu people were one of the earliest major ethnic groups to inhabit Myanmar, settling in the region since the early AD periods. The Pyu culture is of great importance when studying the… See the full description on the dataset page: https://huggingface.co/datasets/kalixlouiis/burmese-pyu-character-recognition.Strawflower-Recognition-Image-Dataset
Strawflower Recognition Image Dataset
In the agriculture, forestry, and fisheries domains, horticultural management and plant care face significant challenges. Improving accuracy and efficiency through automation and intelligent systems is a crucial transformation direction for the industry. Existing solutions often use traditional manual recognition or simple feature matching methods, which are easily affected by external environments and are inefficient. This dataset aims to solve… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Strawflower-Recognition-Image-Dataset.Garden-Plant-Tamarisk-Recognition-Image-Dataset
Garden Plant Tamarisk Recognition Image Dataset
In agricultural and garden management, accurately identifying and classifying flowers is an important and challenging task. Traditional manual recognition methods are inefficient, easily influenced by the professional capability of the identifier, and difficult to promote in large-scale applications. Existing automated solutions, though somewhat effective, are often limited by insufficient data and inaccurate annotations. The… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Garden-Plant-Tamarisk-Recognition-Image-Dataset.Ornamental-Flowers-Plum-Recognition-Image-Dataset
Ornamental Flowers Plum Recognition Image Dataset
In the current field of agriculture, forestry, and fisheries, plant recognition, especially the recognition of plum varieties, faces challenges of low efficiency and insufficient accuracy in manual recognition. Existing solutions largely depend on human experience and simple image retrieval, which are inadequate to meet the recognition needs under complex varieties and environments. This dataset aims to enhance the automation and… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Ornamental-Flowers-Plum-Recognition-Image-Dataset.Garden-Flower-Conical-Hydrangea-Image-Recognition-Dataset
Garden Flower Conical Hydrangea Image Recognition Dataset
With the rapid development of the landscaping industry, garden plants, especially flowers, have a wide variety, making accurate identification and classification a major challenge for the industry. Existing manual identification and traditional image recognition methods have shortcomings such as being time-consuming and having low accuracy. The construction of this dataset aims to enhance the accuracy and efficiency of… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Garden-Flower-Conical-Hydrangea-Image-Recognition-Dataset.Pansy-Recognition-Image-Dataset
Pansy Recognition Image Dataset
Currently, garden management faces the challenge of efficiently and accurately identifying flower varieties. Traditional manual identification relies on experience and is inefficient. Existing image recognition technologies still need improvement in the accuracy of specific flower types, especially in complex backgrounds. This dataset aims to address common accuracy deficiencies in pansy recognition by providing a large number of high-quality images… See the full description on the dataset page: https://huggingface.co/datasets/shangzx/Pansy-Recognition-Image-Dataset.Traffic-Congestion-Recognition-Dataset
Traffic Congestion Recognition Dataset
The main challenge currently faced by the transportation industry is the increasingly severe urban congestion problem, leading to low traffic efficiency and aggravated environmental pollution. Existing traffic monitoring systems often rely on static data, lacking real-time performance and flexibility. To solve this problem, this dataset aims to provide high-quality congestion recognition data to support real-time decision-making and management… See the full description on the dataset page: https://huggingface.co/datasets/shangzx/Traffic-Congestion-Recognition-Dataset.Face-Recognition-Image-Dataset
Face Recognition Image Dataset
Currently, face recognition is widely used in smart devices, but factors such as environmental changes and lighting effects pose challenges to recognition accuracy. Existing datasets often lack diversity and annotation quality, limiting the algorithm's performance improvement. This dataset aims to improve the accuracy of recognition algorithms by providing a rich diversity of high-quality face images. During data collection, various types of camera… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Face-Recognition-Image-Dataset.