Context STL-10 is an image recognition dataset inspired by CIFAR-10 dataset with some improvements. With a corpus of 100,000 unlabeled images and 500 training images, this dataset is best for developing unsupervised feature learning, deep learning, self-taught learning algorithms. Unlike CIFAR-10, the dataset has a higher resolution which makes it a challenging benchmark for developing more scalable unsupervised learning methods. Content Data overview: - There are three files: train_image.zips, test_images.zip and unlabeled_images.zip - 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 - 100,000 unlabeled images for unsupervised learning. These examples are extracted from a similar but broader distribution of images. For instance, it contains other types of animals (bears, rabbits, etc.) and vehicles (trains, buses, etc.) in addition to the ones in the labeled set - Images were acquired from labeled examples on ImageNet The original data source recommends the following standardized testing protocol for reporting results: 1. Perform unsupervised training on the unlabeled data 2. Perform supervised training on the labeled data using 10 (pre-defined) folds of 100 examples from the training data. The indices of the examples to be used for each fold are provided 3. Report average accuracy on the full test set Acknowledgements Original data source and banner image: https://cs.stanford.edu/~acoates/stl10/ Please cite the following reference when using this dataset: Adam Coates, Honglak Lee, Andrew Y. Ng An Analysis of Single Layer Networks in Unsupervised Feature Learning AISTATS, 2011. Inspiration - Can you train a model to accurately identify what animal or transportation object is in each image?