datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Defactify_Image_Dataset
Defactify_Image_Dataset
This dataset is associated with the paper A Comprehensive Dataset for Human vs. AI Generated Image Detection.
📝 Dataset Description
Dataset Summary
The Defactify_Image_Dataset (A Comprehensive Dataset for Human vs. AI Generated Image Detection) is a high-quality collection of 96,000 images and associated metadata designed to benchmark models for detecting and identifying the source of artificially generated content. Built using the MS… See the full description on the dataset page: https://huggingface.co/datasets/Rajarshi-Roy-research/Defactify_Image_Dataset.ai-image-detection-dataset
AI-Image Detection Dataset
Paired real / AI images, with shared image-grounded captions, for training and
evaluating AI-generated-image detectors.
Each of 10,000 real photos is captioned once (BLIP-2) and paired with one synthetic
partner per generator (6 generators → 60,000 AI images). A real image and all of its
AI partners share the same prompt, so the only systematic difference between the
classes is the generative process itself. A detector trained here is pushed toward the… See the full description on the dataset page: https://huggingface.co/datasets/Shanmuk4622/ai-image-detection-dataset.text-2-image-human-preferences-2m
Text-to-image human preferences: 2M votes across 30 models
This dataset contains the complete voting record behind the
Datapoint Image Bench
leaderboard: 2,161,160 validated pairwise votes — exactly 10 for each of
216,116 image pairs. The votes compare 30 text-to-image models in a complete
round-robin on 500 prompts, judged by annotators from over 200 countries.
Every vote includes the annotator's trust score at the time the vote was
cast.
Built on the Datapoint annotation… See the full description on the dataset page: https://huggingface.co/datasets/datapointai/text-2-image-human-preferences-2m.Textual-Image-Caption-Dataset
Update: OCT-2023
Add v2 with recent SoTA model swinV2 classifier for both soft/hard-label visual_caption_cosine_score_v2 with person label (0.2, 0.3 and 0.4)
Introduction
Modern image captaining relies heavily on extracting knowledge, from images such as objects,
to capture the concept of static story in the image. In this paper, we propose a textual visual context dataset
for captioning, where the publicly available dataset COCO caption (Lin et al., 2014) has been… See the full description on the dataset page: https://huggingface.co/datasets/AhmedSSabir/Textual-Image-Caption-Dataset.StreetView-Image-Dataset-10K
Urban Streetscape Dataset for Vision Language Models
A curated subset of 10,000 street view images with 25 essential features optimized for training vision language models on urban environment analysis tasks.
Dataset Description
This dataset contains street view imagery paired with comprehensive annotations covering infrastructure characteristics, visual perception metrics, environmental context, and semantic segmentation data.
This comprehensive dataset represents a… See the full description on the dataset page: https://huggingface.co/datasets/Sadhana-24/StreetView-Image-Dataset-10K.text-2-image-dpo-human-preferences-full
Text-2-Image DPO Human Preferences (Full)
The complete human preference dataset for text-to-image generation. 416,360 pairwise judgments from ~20,000 annotators comparing AI-generated images across two evaluation dimensions: prompt alignment and overall preference.
This is the full, unfiltered version with uniform vote weights. For quality-filtered subsets with calibrated annotator weighting, see:
datapointai/text-2-image-dpo-human-preferences (5,000 pairs, trust-weighted)… See the full description on the dataset page: https://huggingface.co/datasets/datapointai/text-2-image-dpo-human-preferences-full.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.2026-24679-image-dataset
24-679 (Fall 2026): Sweet and Savory Food Images
kwongnon/2026-24679-image-dataset
A binary image-classification dataset of sweet and savory foods, prepared as square RGB images for
training and evaluating image-classification models. Images are organized into two classes:
0 = sweet and 1 = savory.
The intended use is a classroom machine-learning exercise focused on image preprocessing,
augmentation, transfer learning, and model evaluation rather than production food… See the full description on the dataset page: https://huggingface.co/datasets/kwongnon/2026-24679-image-dataset.2026-24679-image-dataset
24-679 (Fall 2026): Campus Recycling and Trash Bins
ccm/2026-24679-image-dataset
Classroom photographs of campus recycling and trash bins, prepared as square RGB images with four
separately generated training variants. The intended exercise is transfer learning and evaluation on
a small image collection, rather than deployment as a waste-sorting system.
Source and task
The preparation notebook uses the Google Forms export Image Data.csv and a ZIP of the form's… See the full description on the dataset page: https://huggingface.co/datasets/ccm/2026-24679-image-dataset.Smart-Projector-Image-Classification-Dataset
Smart Projector Image Classification Dataset
With the rapid development of smart devices, a variety of portable projectors have emerged in the market. In practical applications, accurately recognizing and classifying the appearance images of these projectors has become a technical challenge. Current image recognition technology often yields poor classification results due to insufficient datasets or inaccurate annotations when dealing with different brands and models of projectors.… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Smart-Projector-Image-Classification-Dataset.Beauty-Aid-Tools-Image-Classification-Dataset
Beauty Aid Tools Image Classification Dataset
With the rapid development of the retail e-commerce industry, the market demand for beauty aid tools is continuously increasing. However, current image classification technologies face many challenges, such as diverse product forms and complex usage scenarios, leading to low accuracy and efficiency in product recognition. Existing solutions often rely on manual labeling, which involves a large amount of work and is difficult to maintain.… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Beauty-Aid-Tools-Image-Classification-Dataset.2026-24679-image-dataset
24-679 (Fall 2026): 3D-Printed vs Manufactured Household Objects
kadireks/2026-24679-image-dataset
A binary image-classification dataset of everyday objects, prepared as square RGB images for training
and evaluating image-classification models. Images are organized into two classes:
0 = not 3D printed and 1 = 3D printed.
The intended use is a classroom machine-learning exercise focused on image preprocessing,
augmentation, transfer learning, and model evaluation rather than… See the full description on the dataset page: https://huggingface.co/datasets/kadireks/2026-24679-image-dataset.2026-mug-image-dataset
Mug Classification Dataset
pcwoods/2026-mug-image-dataset
This dataset contains tabletop photographs labeled for the presence of one or more mugs. Images are prepared as 224x224 RGB squares with gray padding.
Source
All images for this dataset were hand-captured by the author and hand-labeled.
Dataset Structure
image: 224x224 RGB image.
label: 0 = No mug, 1 = Mug.
provenance: includes source_id, parent_id, and augmentation type.… See the full description on the dataset page: https://huggingface.co/datasets/pcwoods/2026-mug-image-dataset.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.core_sample_image_data
🖼 Soil Core Sample Image Data
This dataset contains 3604 labeled images of soil core samples for image classification.
📌 Dataset Summary
Images: Squared images (300x300 pixels), cropped from full-scale high-resolution images of soil core samples.
Labels: hb, nb (terms according to DIN 4023:2006-02).
Format: Hugging Face datasets.Dataset with Image() feature.
Split: Train / val / test split is performed with a ratio of 0.8 / 0.1 / 0.1, whereas the samples are stratified… See the full description on the dataset page: https://huggingface.co/datasets/grano1/core_sample_image_data.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.figma_image_dataset_samples
InfoBay.AI Design Assets Dataset Collection
Overview
The InfoBay.AI Design Assets Dataset Collection is a comprehensive multimodal dataset containing more than 2.37 million professionally curated digital design assets across multiple creative domains. The collection has been prepared for Artificial Intelligence, Machine Learning, Computer Vision, Design Automation, Multimodal Foundation Models, Enterprise Search, Retrieval Systems, UI/UX Research, and Generative… See the full description on the dataset page: https://huggingface.co/datasets/InfoBayAI/figma_image_dataset_samples.Infusion-Bottle-Liquid-Type-Classification-Image-Dataset
Infusion Bottle Liquid Type Classification Image Dataset
The current medical industry faces challenges in liquid infusion management, such as inaccurate classification and low processing efficiency. Traditional manual classification methods often rely on experience, which can lead to errors. Existing solutions lack sufficient training data, resulting in inadequate accuracy of intelligent classification models. This dataset aims to provide a rich collection of liquid type image data… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Infusion-Bottle-Liquid-Type-Classification-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.Durian-Plantation-Disease-Leaf-Rot-Detection-Image-Dataset
Durian Plantation Disease Leaf Rot Detection Image Dataset
Ensures high-quality data through multiple rounds of annotation and automated consistency checks, combined with reviews by agricultural pathology experts. The annotation team consists of 10 professionals in agriculture and computer vision. Pre-processing steps include noise reduction, size adjustments, and color normalization to enhance the model's recognition capabilities. Data is stored in JPG format, organized… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Durian-Plantation-Disease-Leaf-Rot-Detection-Image-Dataset.Orchid-Identification-Image-Dataset
Orchid Identification Image Dataset
In modern agriculture and horticultural management, quickly and accurately identifying specific plant species like orchids is important for improving production efficiency and carrying out scientific research. However, due to the large number of plant species and morphological similarities, traditional manual identification methods are time-consuming and labor-intensive, and prone to misjudgment. Therefore, there is an urgent need for an efficient… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Orchid-Identification-Image-Dataset.Fashion-T-Shirt-Occlusion-Image-Dataset
Fashion T-Shirt Occlusion Image Dataset
The retail e-commerce industry faces significant challenges in accurately recognizing and recommending fashion items due to occlusion issues in images. Current solutions often fall short in handling overlapping clothing items or partial visibility, leading to poor customer experience and lost sales opportunities. This dataset aims to address these challenges by providing a comprehensive collection of images depicting T-shirts with various… See the full description on the dataset page: https://huggingface.co/datasets/shangzx/Fashion-T-Shirt-Occlusion-Image-Dataset.Maternity-Wear-Image-Classification-Dataset
Maternity Wear Image Classification Dataset
With the rapid development of the e-commerce industry, the demand for maternity wear is gradually increasing. However, current image classification technology still faces challenges in accuracy and data scarcity when identifying different styles of maternity wear. Most existing solutions rely on manual labeling, which is inefficient and prone to errors. This dataset aims to improve the accuracy of automated classification by providing… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Maternity-Wear-Image-Classification-Dataset.Sunscreen-Products-Image-Classification-Dataset
Sunscreen Products Image Classification Dataset
Currently, the retail e-commerce industry faces challenges such as low efficiency in product classification and difficulties in consumer search, especially with sunscreen products, where customers find it hard to quickly locate the required items. Existing image classification solutions lack accuracy and response speed. This dataset aims to improve the accuracy and efficiency of product recognition through high-quality images of… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Sunscreen-Products-Image-Classification-Dataset.Smart-Air-Conditioner-Image-Classification-Dataset
Smart Air Conditioner Image Classification Dataset
The current smart device industry faces challenges such as increased complexity in user interaction and insufficient accuracy in device recognition. Existing solutions often rely on traditional image recognition technology, resulting in low accuracy and poor adaptability. The construction of the Smart Air Conditioner Image Classification Dataset aims to address this issue by providing high-quality image data to help models better… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Smart-Air-Conditioner-Image-Classification-Dataset.Eyebrow-Makeup-Product-Image-Classification-Dataset
Eyebrow Makeup Product Image Classification Dataset
The current retail e-commerce industry faces the challenge of a wide variety of products and diverse customer needs, especially in the recognition and classification of eyebrow makeup products. Traditional manual annotation methods are inefficient and prone to errors. Existing solutions often fail to meet the requirements for high precision and efficiency, thus there is an urgent need for a high-quality image classification dataset… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Eyebrow-Makeup-Product-Image-Classification-Dataset.Bottled-Goods-Image-Classification-Dataset
Bottled Goods Image Classification Dataset
The retail e-commerce industry currently faces challenges such as low efficiency and poor accuracy in product classification, affecting inventory management and user experience. Existing image classification solutions often rely on limited sample sizes and simple feature extraction methods, resulting in insufficient model generalization capability. This dataset aims to address the problem of insufficient samples in image classification by… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Bottled-Goods-Image-Classification-Dataset.Parent-Child-Outfit-Image-Classification-Dataset
Parent-Child Outfit Image Classification Dataset
With the rapid development of retail e-commerce, the parent-child outfit market is also gradually expanding, and consumer demand for parent-child outfit products is increasing. However, existing product classification systems often fail to accurately identify and classify parent-child outfits, leading to poor user experience and affecting sales performance. Current solutions lack in labeling accuracy and data integrity, making it… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Parent-Child-Outfit-Image-Classification-Dataset.Household-Paper-Products-Occlusion-Image-Dataset
Household Paper Products Occlusion Image Dataset
Currently, the retail e-commerce industry faces challenges in accurately detecting and categorizing household paper products due to varying packaging designs and occlusions in images. Existing datasets often lack diversity in terms of occlusion scenarios and packaging types, leading to reduced model performance in real-world applications. This dataset aims to address these issues by providing a comprehensive collection of images… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Household-Paper-Products-Occlusion-Image-Dataset.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.