The STL-10 is an image dataset derived from ImageNet and popularly used to evaluate algorithms of unsupervised feature learning or self-taught learning. Besides 100,000 unlabeled images, it contains 13,000 labeled images from 10 object classes (such as birds, cats, trucks), among which 5,000 images are partitioned for training while the remaining 8,000 images for testing. All the images are color images with 96×96 pixels in size.

The dataset used in the paper is MNIST, CIFAR10 and STL10. These are datasets for image classification tasks.

![]() The STL-10 dataset is an image recognition dataset for developing unsupervised feature learning, deep learning, self-taught learning algorithms. It is inspired by the CIFAR-10 dataset but with some modifications. In particular, each class has fewer labeled training examples than in CIFAR-10, but a very large set of unlabeled examples is provided to learn image models prior to supervised training. The primary challenge is to make use of the unlabeled data (which comes from a similar but different distribution from the labeled data) to build a useful prior. We also expect that the higher resolution of this dataset (96x96) will make it a challenging benchmark for developing more scalable unsupervised learning methods. Overview 10 classes: airplane, bird, car, cat, deer, dog, horse, monkey, ship, truck. Images are 96x96 pixels, color. 500 training images (10 pre-defined folds), 800 test images per class. 100000 unlabeled images for uns

Abstract

In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before.

Dataset

This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from STL10. All zoos with extensive information and code can be found at www.modelzoos.cc.

This repository contains the raw model zoos as collections of models (file names beginning with "cifar_"). Zoos are trained with small and large CNN models, in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). Due to the large filesize, the preprocessed datasets are hosted in a separate repository. The index_dict.json files contain information on how to read the vectorized models.

For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.

STL-10 (Test set only)

Original paper: An Analysis of Single Layer Networks in Unsupervised Feature Learning Homepage: https://cs.stanford.edu/~acoates/stl10/ Bibtex: @InProceedings{pmlr-v15-coates11a, title = {An Analysis of Single-Layer Networks in Unsupervised Feature Learning}, author = {Coates, Adam and Ng, Andrew and Lee, Honglak}, booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics}, pages = {215--223}… See the full description on the dataset page: https://huggingface.co/datasets/djghosh/wds_stl10_test.

The dataset used in the paper is a mixture of Gaussians, CIFAR-10, STL-10, CelebA, and ImageNet.

Datasets:

• Asirra
• CIFAR-10
• CIFAR-100
• GTSRB
• HASYv2
• MNIST
• STL-10
• SVHN

The dataset used in the paper is a de-noising diffusion probabilistic model (DDPM) trained on CIFAR-10, CIFAR-100, STL-10, and Tiny-ImageNet.

Classification performance of the WideResNet-16 architecture on the STL-10 dataset.

Dataset Card for STL-10 Cleaned (Deduplicated Training Set)

  Dataset Description

This dataset is a modified version of the STL-10 dataset. The primary modification involves deduplicating the training set by removing any images that are exact byte-for-byte matches (based on SHA256 hash) with images present in the original STL-10 test set. The dataset comprises this cleaned training set and the original, unmodified STL-10 test set. The goal is to provide a cleaner separation… See the full description on the dataset page: https://huggingface.co/datasets/Shu1L0n9/CleanSTL-10.

The dataset used in the paper is a ResNet trained on various datasets, including MNIST, Fashion MNIST, CIFAR10, STL10, and CIFAR100.