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 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

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

STL10-Labeled

This public repo contains labels for the unlabeled pictures in the stl10 dataset. Please refer to files Files and versions tab above. You can also refer to my original repo https://github.com/semihyagli/STL10-Labeled Please consider sponsoring this repo so that we can continue to develop high-quality datasets for the ML/AI research. To become a sponsor: GitHub Sponsors Buy me a coffee You can also sponsor us by downloading our free application, Etiqueta, to your… See the full description on the dataset page: https://huggingface.co/datasets/semihyagli/STL10-Labeled.

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

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.

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.

Datasets:

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

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.

STL10 - Segmentation

Please consider sponsoring this repo so that we can continue to develop high-quality datasets for the AI and ML research. To become a sponsor: GitHub Sponsors Buy me a coffee You can also sponsor us by downloading our free application, Etiqueta, to your devices: Etiqueta on iOS or Apple Chip Macs Etiqueta on Android This repo contains segmented images for the labeled part of the STL-10 Dataset.
If you are looking for STL10-Labeled variant of the dataset… See the full description on the dataset page: https://huggingface.co/datasets/semihyagli/STL10-Segmented.

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

This dataset was created by siminyu7_qq

Contents

STL-files for 3D printing. Corresponding to Figures 6-10.

The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe navigation and to provide background data for engineers, scientific, and other commercial and industrial activities. Hydrographic survey data primarily consist of water depths, but may also include features (e.g. rocks, wrecks), navigation aids, shoreline identification, and bottom type information. NOAA is responsible for archiving and distributing the source data as described in this metadata record.

Graph and download economic data for 10-Year Treasury Constant Maturity Minus Federal Funds Rate from 1962-01-02 to 2025-06-23 about yield curve, spread, 10-year, maturity, Treasury, federal, interest rate, interest, rate, and USA.

About this dataset

Context

The dataset tabulates the St. Louis population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for St. Louis. The dataset can be utilized to understand the population distribution of St. Louis by age. For example, using this dataset, we can identify the largest age group in St. Louis.

Key observations

The largest age group in St. Louis, MO was for the group of age 25 to 29 years years with a population of 29,055 (9.91%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in St. Louis, MO was the 80 to 84 years years with a population of 4,112 (1.40%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the St. Louis is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of St. Louis total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for St. Louis Population by Age. You can refer the same here

This dataset provides information about the number of properties, residents, and average property values for 10th Street cross streets in East Saint Louis, IL.

These cylinder geometries are used to verify models in HemeLB. They have cross-sections approximated by 128 edges and feature radius-to-length ratios of 1:10 for cylinder.stl and cylinder_deg20.stl, and 1:20 for cylinder_extended.stl. The cylinder.stl geometry is aligned along the z-axis, while cylinder_deg20.stl is a 20-degree rotation of cylinder.stl. Provided in STL format, these geometries are defined with arbitrary units. Users can create the simulation domain for HemeLB in GMY format from this geometry by using the voxeliser available here.

View the average 10-year expectation for the inflation rate among market participants, based upon Treasury securities.