datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
aidovecl-vehicle-detection-classification-localization
AIDOVECL: AI-generated Dataset of Outpainted Vehicles for Eye-level Classification and Localization
We introduce an annotated AI-generated dataset of eye-level vehicle images using outpainting, offering versatile generation of diverse vehicle classes in varied contexts with pretrained models.
Citation Notice
Please ensure that all publications and presentations using this data reference the following paper:
Kazemi, A., Fatima, Q. ul A., Kindratenko, V., & Tessum, C. W.… See the full description on the dataset page: https://huggingface.co/datasets/amir-kazemi/aidovecl-vehicle-detection-classification-localization.data-csgo-weapon-classification
Dataset for project: csgo-weapon-classification
Dataset Description
This dataset has for project csgo-weapon-classification was collected with the help of a bulk google image downloader.
Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
Data Instances
A sample from this dataset looks as follows:
[
{
"image": "<1768x718 RGB PIL image>",
"target": 0
},
{
"image": "<716x375 RGBA PIL image>"… See the full description on the dataset page: https://huggingface.co/datasets/Kaludi/data-csgo-weapon-classification.Garbage_Classification_YOLONotice: train set include 80% of original dataset, test and val sets have 10%.
intel-image-classification
Intel Image Classification
The Intel Image Classification dataset contains images of natural scenes categorized into six classes:
Buildings
Forest
Glacier
Mountain
Sea
Street
📆 Content
The dataset contains ~25,000 images of size 150x150 pixels.
Images are evenly distributed across 6 categories:
{'buildings' -> 0,
'forest' -> 1,
'glacier' -> 2,
'mountain' -> 3,
'sea' -> 4,
'street' -> 5 }
It is divided into three parts:
Training set: ~14… See the full description on the dataset page: https://huggingface.co/datasets/sfarrukhm/intel-image-classification.AfriMCQA-category-classification
Afri-MCQA cross-modal cultural category classification (MTEB)
Classify the cultural category of an entry from its photograph and the question
about it spoken by a native speaker, across 16 African languages.
Labels index this list:
geography, building, and landmarks
public figure and pop culture
cooking and food
objects, materials, clothing
tranditions, art, and history
brands, products, and companies
plants and animals
people, and everyday life
vehicles and transportation… See the full description on the dataset page: https://huggingface.co/datasets/vnahata/AfriMCQA-category-classification.happy-whale-dolphin-classificationchest-xray-classification
Dataset Labels
['NORMAL', 'PNEUMONIA']
Number of Images
{'train': 4077, 'test': 582, 'valid': 1165}
How to Use
Install datasets:
pip install datasets
Load the dataset:
from datasets import load_dataset
ds = load_dataset("keremberke/chest-xray-classification", name="full")
example = ds['train'][0]
Roboflow Dataset Page
https://universe.roboflow.com/mohamed-traore-2ekkp/chest-x-rays-qjmia/dataset/2
Citation… See the full description on the dataset page: https://huggingface.co/datasets/keremberke/chest-xray-classification.Plant_Detection_Classification
Plant Species Classification Dataset
A comprehensive dataset containing 64 different plant species with high-quality images for machine learning and computer vision applications.
Model Trainig code and trainned model with detailed performance analysis is present on the Github
https://github.com/jameelkhalidawan/Plant-Detection-Model-using-Yolo
Dataset Overview
This dataset is designed for plant species classification tasks and contains images of various plant… See the full description on the dataset page: https://huggingface.co/datasets/jameelkhalidawan/Plant_Detection_Classification.multimodal_meme_classification_singapore
Dataset Card for Offensive Memes in Singapore Context
Dataset Details
Dataset Description
This dataset is a collection of memes from various existing datasets, online forums, and freshly scrapped contents. It contains both global-context memes and Singapore-context memes, in different splits. It has textual description and a label stating if it is offensive under Singapore society's standards.
Curated by: Cao Yuxuan, Wu Jiayang, Alistair Cheong, Theodore Lee… See the full description on the dataset page: https://huggingface.co/datasets/aliencaocao/multimodal_meme_classification_singapore.fruit-classification
Fruit Classification Dataset
This dataset contains images of fruits classified into six categories:
fresh and rotten apples, bananas, and oranges.
Classes
fresh_apple
fresh_banana
fresh_orange
rotten_apple
rotten_banana
rotten_orange
Structure
Images stored in train/ and test/
Labels provided in train.csv
Usage
from datasets import load_dataset
dataset = load_dataset("menasi11/fruit-classification")
print(dataset)
plant_classification_v11hagrid-classification-512p-dataset
Dataset Card for "hagrid-classification-512p-dataset"
More Information needed
vqa_plant-disease-classification-merged-datasetdata-food-classification
Dataset for project: food-classification
Dataset Description
This dataset has been processed for project food-classification.
Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
Data Instances
A sample from this dataset looks as follows:
[
{
"image": "<308x512 RGB PIL image>",
"target": 0
},
{
"image": "<512x512 RGB PIL image>",
"target": 0
}]
Dataset Fields
The dataset has the… See the full description on the dataset page: https://huggingface.co/datasets/Kaludi/data-food-classification.autotrain-data-galaxy_classification
AutoTrain Dataset for project: galaxy_classification
Dataset Description
This dataset has been automatically processed by AutoTrain for project galaxy_classification.
Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
Data Instances
A sample from this dataset looks as follows:
[
{
"image": "<256x256 RGB PIL image>",
"target": 0
},
{
"image": "<256x256 RGB PIL image>",
"target": 0
}]… See the full description on the dataset page: https://huggingface.co/datasets/Xanadu00/autotrain-data-galaxy_classification.Pothole_classificationA dataset for efficient pothole classification which contains more than 400 images collected over 50 km road from kerala.
apple_leaf_disease_classification
Apple Leaf Disease Classification
A dataset for image classification of Apple Leaf Disease Classification. The dataset contains 7,505 images across 3 classes: Alternaria, Apple_Mosaic, Healthy.Images per class:
Alternaria: 2,523
Apple_Mosaic: 2,523
Healthy: 2,459
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{yatoo2024indigenous,
title={An indigenous dataset for the detection and classification… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/apple_leaf_disease_classification.cassava-leaf-disease-classificationrecursion-cellular-image-classification-datasetfood-category-classification-v2.0
Dataset for project: food-category-classification-v2.0
Dataset Description
This dataset for project food-category-classification-v2.0 was scraped with the help of a bulk google image downloader.
Dataset Structure
Dataset Fields
The dataset has the following fields (also called "features"):
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['Bread', 'Dairy', 'Dessert', 'Egg', 'Fried Food', 'Fruit', 'Meat', 'Noodles', 'Rice'… See the full description on the dataset page: https://huggingface.co/datasets/Kaludi/food-category-classification-v2.0.bruised_vegetable_classification
Bruised Vegetable Classification
A dataset for classification of Bruised Vegetable Classification. The dataset contains 4,464 images across 3 classes.
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{samanta2025nature,
title={Nature's best vs. bruised: A veggie edibility evaluation database},
author={Samanta, Bidisha and Banerjee, Sriparna and Das, Ranadhir and Chaudhuri, Sheli Sinha and Djemal… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/bruised_vegetable_classification.human-nonhuman-face-classification
Human vs Non-Human Face Dataset
A robust dataset for binary classification between real human faces and non-human face-like objects (statues, art, gaming, anime).
📊 Dataset Statistics
Split
Human
Non-Human
Total
Train
3,024
2,949
5,973
Validation
864
842
1,706
Test
433
422
855
Total
8,534
📁 Format
Labels: 0: human, 1: non_human.
🚀 Quick Start
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/LakoreAI/human-nonhuman-face-classification.smoking_classificationagarwood_leaf_disease_classification
Agarwood Leaf Disease Classification
A dataset for disease classification of agarwood leaves. The dataset contains 5,472 images across 14 classes: Anthracnose, Black spots, Brown clumps, Brown spots, Downy mildew, Flea Beetles, Healthy, Mealy bugs, Mosaic Viruses, Powdery mildew, Scale insect, Sooty mold, Spiders, Translucent lesion.Images per class:
Anthracnose: 232
Black spots: 674
Brown clumps: 118
Brown spots: 1,055
Downy mildew: 674
Flea Beetles: 115
Healthy: 415
Mealy… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/agarwood_leaf_disease_classification.TN5000-thyroid-nodule-classification
TN5000 Thyroid Nodule Classification Dataset
A preprocessed, CNN-ready version of the TN5000 thyroid ultrasound dataset, cropped to individual nodule regions of interest and standardized to 224×224 PNG images for binary classification (Benign vs. Malignant).
Source Dataset
This dataset is derived from:
TN5000: An Ultrasound Image Dataset for Thyroid Nodule Detection and ClassificationXiaoxian Yu et al., Scientific Data (Nature Publishing Group), 2025DOI:… See the full description on the dataset page: https://huggingface.co/datasets/Johnyquest7/TN5000-thyroid-nodule-classification.plant_doc_classification
Plant Doc Classification
A dataset for disease classification of various plants. The dataset contains 2,569 images across 28 classes:Images per class:
Apple Scab Leaf: 93
Apple leaf: 91
Apple rust leaf: 88
Bell_pepper leaf: 61
Bell_pepper leaf spot: 71
Blueberry leaf: 114
Cherry leaf: 57
Corn Gray leaf spot: 68
Corn leaf blight: 191
Corn rust leaf: 116
Peach leaf: 111
Potato leaf early blight: 116
Potato leaf late blight: 105
Raspberry leaf: 119
Soyabean leaf: 65
Squash Powdery… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/plant_doc_classification.painting-style-classification
Dataset Labels
['Realism', 'Art_Nouveau_Modern', 'Analytical_Cubism', 'Cubism', 'Expressionism', 'Action_painting', 'Synthetic_Cubism', 'Symbolism', 'Ukiyo_e', 'Naive_Art_Primitivism', 'Post_Impressionism', 'Impressionism', 'Fauvism', 'Rococo', 'Minimalism', 'Mannerism_Late_Renaissance', 'Color_Field_Painting', 'High_Renaissance', 'Romanticism', 'Pop_Art', 'Contemporary_Realism', 'Baroque', 'New_Realism', 'Pointillism', 'Northern_Renaissance', 'Early_Renaissance'… See the full description on the dataset page: https://huggingface.co/datasets/keremberke/painting-style-classification.pose-classificationdata-food-category-classification
Dataset for project: food-category-classification
Dataset Description
This dataset is for project food-category-classification.
Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
Data Instances
A sample from this dataset looks as follows:
[
{
"image": "<512x512 RGB PIL image>",
"target": 0
},
{
"image": "<512x512 RGB PIL image>",
"target": 0
}]
Dataset Fields
The dataset has… See the full description on the dataset page: https://huggingface.co/datasets/Kaludi/data-food-category-classification.corn_leaf_pest_classification
Corn Leaf Pest Classification
A dataset for image classification of Corn Leaf Pest Classification.The raw dataset contains 1,308 images.The augmented dataset contains 11,772 images.
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{tchokogoue2025towards,
title={Towards precision agriculture: A dataset for early detection of corn leaf pests},
author={Tchokogou{\'e}, Thierry and Noumsi, Auguste… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/corn_leaf_pest_classification.