Published July 2, 2026
| Version v1
Dataset
Open
Embedded representations of popular image datasets (CIFAR10, CIFAR100, CIFAR100coarse, MNIST, FashionMNIST, SVHN)
Description
Vectorial representations for common image datasets:
- CIFAR10, CIFAR100, CIFAR100coarse
- MNIST, FashionMNIST
- SVHN
Extracted using the scripts in this repo, where more details of the extraction can be consulted.
The embeddings are provided in different modalities:
- features: the next-to-last representation of the network
- logits: the pre-softmax values
- predictions: the post-softmax values (posterior probabilities)
- targets: the true labels
Each file is stored in format npz (dictionary-like) and contains the numpy representations for the keys "train", "val", and "test" partitions for each dataset. The "train" partition was used to train the corresponding neural model, "val" was used for training monitoring, and "test" was not used during training.
The name of the file uses the format <dataset>_<neural-model>_<modality>.npz; for example: "svhn_resnet18_logits.npz".
See reference list for publications related to each dataset.
Files
Files
(728.6 MB)
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md5:c8f215ba84b558377c73b93a75e2382a
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Additional details
Software
- Repository URL
- https://github.com/pglez82/visiondatasets_quapy
- Programming language
- Python
- Development Status
- Active
References
- CIFAR10, CIFAR100, CIFAR100coarse: Alex Krizhevsky and Geoffrey Hinton. Learning multiple layers of features from tiny images. Technical report, University of Toronto, Toronto, Ontario, 2009.
- MNIST: Yann LeCun, Corinna Cortes, and Christopher J. C. Burges. The MNIST database of handwritten digits. http://yann.lecun.com/exdb/mnist/, 1998.
- FashionMNIST: Han Xiao, Kashif Rasul, and Roland Vollgraf. Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747, 2017.
- SVHN: Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Baolin Wu, Andrew Y Ng, et al. Reading digits in natural images with unsupervised feature learning. In NIPS workshop on deep learning and unsupervised feature learning, volume 2011, page 4. Granada, 2011.