datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
cifar10
Dataset Card for CIFAR-10
Dataset Summary
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain… See the full description on the dataset page: https://huggingface.co/datasets/mteb/cifar10.tiny-imagenet
Dataset Card for tiny-imagenet
Dataset Summary
Tiny ImageNet contains 100000 images of 200 classes (500 for each class) downsized to 64×64 colored images. Each class has 500 training images, 50 validation images, and 50 test images.
Languages
The class labels in the dataset are in English.
Dataset Structure
Data Instances
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=64x64 at 0x1A800E8E190,
'label': 15
}… See the full description on the dataset page: https://huggingface.co/datasets/mteb/tiny-imagenet.cifar100
Dataset Card for CIFAR-100
Dataset Summary
The CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images
per class. There are 500 training images and 100 testing images per class. There are 50000 training images and 10000 test images. The 100 classes are grouped into 20 superclasses.
There are two labels per image - fine label (actual class) and coarse label (superclass).
Supported Tasks and Leaderboards
image-classification: The… See the full description on the dataset page: https://huggingface.co/datasets/mteb/cifar100.eurosat-rgb
EuroSat (RGB)
Description
A dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000 labeled and geo-referenced samples. This is the RGB version of the dataset with visible bands encoded as JPEG images.
The dataset does not have any default splits. Train, validation, and test splits were based on these definitions here… See the full description on the dataset page: https://huggingface.co/datasets/mteb/eurosat-rgb.food101
Dataset Card for Food-101
Dataset Summary
This dataset consists of 101 food categories, with 101'000 images. For each class, 250 manually reviewed test images are provided as well as 750 training images. On purpose, the training images were not cleaned, and thus still contain some amount of noise. This comes mostly in the form of intense colors and sometimes wrong labels. All images were rescaled to have a maximum side length of 512 pixels.
Supported Tasks and… See the full description on the dataset page: https://huggingface.co/datasets/mteb/food101.resisc45
Description
RESISC45 dataset is a publicly available benchmark for Remote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This dataset contains 31,500 images, covering 45 scene classes with 700 images in each class.
The dataset does not have any default splits. Train, validation, and test splits were based on these definitions here… See the full description on the dataset page: https://huggingface.co/datasets/mteb/resisc45.crisismmd-mteb
CrisisMMD for MTEB
This repository contains two image+text classification configurations derived
from the official QCRI/CrisisMMD
release at revision 10a5626ba112ab9c50369c473a2062bcf205d708.
informative: useful vs. not useful for humanitarian response.
humanitarian: five humanitarian information categories.
For informative and humanitarian, only rows in the authors' official
text_img_agreed_lab splits are retained. Their independently annotated image
and tweet labels are… See the full description on the dataset page: https://huggingface.co/datasets/pranitchawla/crisismmd-mteb.Caltech101
Caltech101
An MTEB dataset
Massive Text Embedding Benchmark
Classifying images of 101 widely varied objects.
Task category
i2c
Domains
Encyclopaedic
Reference
https://ieeexplore.ieee.org/document/1384978
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["Caltech101"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
To… See the full description on the dataset page: https://huggingface.co/datasets/mteb/Caltech101.VOC2007
VOC2007
An MTEB dataset
Massive Text Embedding Benchmark
Classifying bird images from 500 species.
Task category
i2c
Domains
Encyclopaedic
Reference
http://host.robots.ox.ac.uk/pascal/VOC/
Source datasets:
HuggingFaceM4/pascal_voc
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("VOC2007")
evaluator = mteb.MTEB([task])
model = mteb.get_model(YOUR_MODEL)… See the full description on the dataset page: https://huggingface.co/datasets/mteb/VOC2007.FGVCAircraft
FGVCAircraft
An MTEB dataset
Massive Text Embedding Benchmark
Classifying aircraft images from 41 manufacturers and 102 variants.
Task category
i2c
Domains
Encyclopaedic
Reference
https://arxiv.org/abs/1306.5151
Source datasets:
HuggingFaceM4/FGVC-Aircraft
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("FGVCAircraft")
evaluator = mteb.MTEB([task])
model =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/FGVCAircraft.mnist
Dataset Card for MNIST
Dataset Summary
The MNIST dataset consists of 70,000 28x28 black-and-white images of handwritten digits extracted from two NIST databases. There are 60,000 images in the training dataset and 10,000 images in the validation dataset, one class per digit so a total of 10 classes, with 7,000 images (6,000 train images and 1,000 test images) per class.
Half of the image were drawn by Census Bureau employees and the other half by high school students… See the full description on the dataset page: https://huggingface.co/datasets/mteb/mnist.glami-1m-mteb
GLAMI-1M MTEB multimodal classification
This is an MTEB-ready derivative of the official
glami/glami-1m
release for multilingual image+text fashion classification. The source is
pinned at revision befda45d8d4e8b8082bb8a1912d1f9eb9483991c and remains
licensed under Apache-2.0.
Each example contains the official product image, name and description
joined as text, and the official category ID as label. The complete
116,004-row human-labeled test split is unchanged.
To keep… See the full description on the dataset page: https://huggingface.co/datasets/artist/glami-1m-mteb.oxford-flowers
Dataset Card for "oxford-flowers"
More Information needed