datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
cifar100-enrichedThe 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).cifar10
Dataset Specifications
Contains the entire CIFAR10 dataset, downloaded via PyTorch, then split and saved as .png files representing 32x32 images.
There a three splits, perfectly balanced class-wise:
train: 49,000 out of the original 50,000 samples from the training set of CIFAR10;
calibration: 1,000 left-out samples from the training set;
test: 10,000 samples, the entire original test set.
File Structure
Files are archives <split>/<classname>.zip. Each… See the full description on the dataset page: https://huggingface.co/datasets/ego-thales/cifar10.xai-attack-detection-cifar10
XAI Attack Detection — CIFAR-10 PGD
This private research dataset contains balanced, paired clean and adversarial images for
studying whether an attack can be detected from a classifier explanation map.
Dataset construction
The source is the CIFAR-10 test split. A fine-tuned OpenCLIP ViT-B/16 classifies each
clean image. Clean-correct examples are attacked with untargeted L-infinity PGD using
epsilon 8/255, step size 2/255, 10 steps, and deterministic random… See the full description on the dataset page: https://huggingface.co/datasets/nimaeb/xai-attack-detection-cifar10.mlcd-mteb-cifar-eval
MLCD vs CLIP on MTEB CIFAR-10/100: integration and evaluation
Evaluation results accompanying the MTEB integration of two MLCD image encoders
(PR #5406, resolving
issue #2571).
Two DeepGlint-AI MLCD encoders were integrated into MTEB, verified against the
reference implementation, and evaluated on the official MTEB CIFAR-10/CIFAR-100
image-classification tasks alongside size-matched OpenAI CLIP baselines.
What was measured
Official MTEB image classification: 5… See the full description on the dataset page: https://huggingface.co/datasets/b4ph/mlcd-mteb-cifar-eval.cifar10-lance
CIFAR-10 (Lance Format)
A Lance-formatted version of CIFAR-10 covering 60,000 32×32 RGB images across ten balanced object classes. Each row carries inline PNG bytes, the integer label, the human-readable class name, and a cosine-normalized CLIP image embedding, all backed by a bundled IVF_PQ vector index plus scalar indices on the label columns and available directly from the Hub at hf://datasets/lance-format/cifar10-lance/data.
Key features
Inline PNG bytes in the… See the full description on the dataset page: https://huggingface.co/datasets/lance-format/cifar10-lance.svae-freckles-4096-cifar10
SVAE Freckles 4096 — CIFAR-10 Omega Tokens
Precomputed spectral decomposition of CIFAR-10 through Freckles v41 (256×256), a frozen Spectral Variational Autoencoder trained exclusively on synthetic noise.
Each CIFAR-10 image is resized to 256×256, decomposed into 4096 patches (4×4 each), and passed through Freckles' encoder → SVD bottleneck. The 4 singular values per patch are stored as a (4, 64, 64) omega map — a 4-channel spatial representation of spectral energy.… See the full description on the dataset page: https://huggingface.co/datasets/AbstractPhil/svae-freckles-4096-cifar10.CIFAR100-customExample of usage:
from datasets import load_dataset
dataset = load_dataset("Andron00e/CIFAR100-custom")
splitted_dataset = dataset["train"].train_test_split(test_size=0.2)
CIFAR10-customExample of usage:
from datasets import load_dataset
dataset = load_dataset("Andron00e/CIFAR10-custom")
splitted_dataset = dataset["train"].train_test_split(test_size=0.2)
cifar10-stats
CIFAR-10 CNN Layerwise Training Statistics
Dataset Description
This dataset contains layer-wise training statistics for a convolutional network trained on CIFAR-10, together with the corresponding test/acc.
Each row is one point at loss landscape. The features are computed on the last training batch of 1024 samples before the end of an epoch, and test/acc is measured immediately after that epoch.
The dataset includes statistics for several convolutional layers and the… See the full description on the dataset page: https://huggingface.co/datasets/Fullfix/cifar10-stats.Cifar10Mnist
Cifar10Mnist Dataset Card
Dataset Summary
Cifar10Mnist is a synthetic image dataset created by overlaying MNIST digit images on top of CIFAR-10 images. Each example contains a 32x32 RGB image and a paired label tuple: the original CIFAR-10 class name plus the MNIST digit label.
Supported Tasks
Image classification
Multi-label classification
Transfer learning
Synthetic data research
Languages
Not language-specific… See the full description on the dataset page: https://huggingface.co/datasets/shotegni/Cifar10Mnist.cifar10
Dataset Specifications
Contains the entire CIFAR10 dataset, downloaded via PyTorch, then split and saved as .png files representing 32x32 images.
There a three splits, perfectly balanced class-wise:
train: 49,000 out of the original 50,000 samples from the training set of CIFAR10;
calibration: 1,000 left-out samples from the training set;
test: 10,000 samples, the entire original test set.
File Structure
Files are archives <split>/<classname>.zip. Each… See the full description on the dataset page: https://huggingface.co/datasets/xjy0123/cifar10.