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torchvision datasets
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torchvision_datasets_overview.md

Quick summary of all the datasets contained in torchvision.

  • Caltech101: Pictures of objects belonging to 101 categories. About 40 to 800 images per category. Most categories have about 50 images. Collected in September 2003 by Fei-Fei Li, Marco Andreetto, and Marc 'Aurelio Ranzato. The size of each image is roughly 300 x 200 pixels. We have carefully clicked outlines of each object in these pictures, these are included under the 'Annotations.tar'. There is also a matlab script to view the annotaitons, 'show_annotations.m'.
  • Caltech256: Collection of all 30607 images: 256_ObjectCategories.tar
  • CelebA: CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations. The dataset can be employed as the training and test sets for the following computer vision tasks: face attribute recognition, face recognition, face detection, landmark (or facial part) localization, and face editing & synthesis..
  • CIFAR10: The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million tiny images dataset. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class. The classes are completely mutually exclusive. There is no overlap between automobiles and trucks. "Automobile" includes sedans, SUVs, things of that sort. "Truck" includes only big trucks. Neither includes pickup trucks.
  • cityscapes: a diverse set of stereo video sequences recorded in street scenes from 50 different cities, with high quality pixel-level annotations of 5 000 frames in addition to a larger set of 20 000 weakly annotated frames. The Cityscapes Dataset is intended for assessing the performance of vision algorithms for major tasks of semantic urban scene understanding: pixel-level, instance-level, and panoptic semantic labeling; supporting research that aims to exploit large volumes of (weakly) annotated data, e.g. for training deep neural networks.
  • COCO: COCO is a large image dataset designed for object detection, segmentation, person keypoints detection, stuff segmentation, and caption generation. 330K images (>200K labeled). 250,000 people with keypoints. 80 object categories. 91 stuff categories. From Microsoft.
  • FasionMnist: Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.
  • EMNIST: The MNIST database was derived from a larger dataset known as the NIST Special Database 19 which contains digits, uppercase and lowercase handwritten letters. This paper introduces a variant of the full NIST dataset, which we have called Extended MNIST (EMNIST), which follows the same conversion paradigm used to create the MNIST dataset. The result is a set of datasets that constitute a more challenging classification tasks involving letters and digits, and that shares the same image structure and parameters as the original MNIST task, allowing for direct compatibility with all existing classifiers and systems. Benchmark results are presented along with a validation of the conversion process through the comparison of the classification results on converted NIST digits and the MNIST digits. EMNIST ByClass: 814,255 characters. 62 unbalanced classes.
  • HMDB51: HMDB51 is an action recognition video dataset. This dataset consider every video as a collection of video clips of fixed size, specified by frames_per_clip, where the step in frames between each clip is given by step_between_clips.
  • ImageNet: ImageNet is an image database organized according to the WordNet hierarchy (currently only the nouns), in which each node of the hierarchy is depicted by hundreds and thousands of images.
  • Kinetics-400: Kinetics-400 is an action recognition video dataset. This dataset consider every video as a collection of video clips of fixed size, specified by frames_per_clip, where the step in frames between each clip is given by step_between_clips.
  • KITTI: The object detection and object orientation estimation benchmark consists of 7481 training images and 7518 test images, comprising a total of 80.256 labeled objects. All images are color and saved as png.
  • KMINIST: Kuzushiji-MNIST is a drop-in replacement for the MNIST dataset (28x28 grayscale, 70,000 images), provided in the original MNIST format as well as a NumPy format. Since MNIST restricts us to 10 classes, we chose one character to represent each of the 10 rows of Hiragana when creating Kuzushiji-MNIST. Kuzushiji-49, as the name suggests, has 49 classes (28x28 grayscale, 270,912 images), is a much larger, but imbalanced dataset containing 48 Hiragana characters and one Hiragana iteration mark.
  • LSUN: 10 scene categories for LSUN Scene Classification Challange and 20 object categories.
  • Omniglot: 1623 different handwritten characters from 50 different alphabets. Each of the 1623 characters was drawn online via Amazon's Mechanical Turk by 20 different people. Each image is paired with stroke data, a sequences of [x,y,t] coordinates with time (t) in milliseconds.
  • Phototour: The dataset consists of corresponding patches sampled from 3D reconstructions of the Statue of Liberty (New York), Notre Dame (Paris) and Half Dome (Yosemite). Initial point cloud reconstructions were computed using Noah Snavely's Photo Tourism algorithm [Snavely 2008], from which dense depth maps were computed using Michael Goesele's multi-view stereo algorithm [Goesele 2007]. Corresponding interest points were found by mapping between images using the stereo depth maps; this dataset consists of patches sampled around each interest point. Difference of Gaussian and Harris interest points have been used.
  • Place365: The Places365 dataset is a scene recognition dataset. It is composed of 10 million images comprising 434 scene classes. There are two versions of the dataset: Places365-Standard with 1.8 million train and 36000 validation images from K=365 scene classes, and Places365-Challenge-2016, in which the size of the training set is increased up to 6.2 million extra images, including 69 new scene classes (leading to a total of 8 million train images from 434 scene classes).
  • QMNIST: The QMNIST dataset was generated from the original data found in the NIST Special Database 19 with the goal to match the MNIST preprocessing as closely as possible. 60000 samples.
  • SBD: Semantic Boundaries Dataset. The SBD currently contains annotations from 11355 images taken from the PASCAL VOC 2011 dataset. While semantic segmentation aims to predict the pixels that lie inside the object, we are interested in predicting the pixels that lie on the boundary of the object, a task that is arguably harder (or alternatively, an error metric that is arguably more stringent).
  • SEMEION: performing a huge number of Flickr queries and then filtering the noisy results down to 1 million images with associated visually relevant captions. Such a collection allows us to approach the extremely challenging problem of description generation using relatively simple non-parametric methods and produces surprisingly effective results.
  • STL10: 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.
  • SVHN: SVHN is obtained from house numbers in Google Street View images. Over 600,000 digit images and comes from a significantly harder, unsolved, real world problem (recognizing digits and numbers in natural scene images. Replacement to MNIST.
  • UCF101: CF101 is an action recognition data set of realistic action videos, collected from YouTube, having 101 action categories. This data set is an extension of UCF50 data set which has 50 action categories. With 13320 videos from 101 action categories, UCF101 gives the largest diversity in terms of actions and with the presence of large variations in camera motion, object appearance and pose, object scale, viewpoint, cluttered background, illumination conditions, etc, it is the most challenging data set to date.
  • USPS: canned from envelopes by the U.S. Postal Service containing a total of 9,298 16×16 pixel grayscale samples; the images are centered, normalized and show a broad range of font styles.
  • VOC: image data sets for object class recognition
  • WIDERFace: face detection benchmark dataset, of which images are selected from the publicly available WIDER dataset. We choose 32,203 images and label 393,703 faces with a high degree of variability in scale, pose and occlusion as depicted in the sample images. WIDER FACE dataset is organized based on 61 event classes. For each event class, we randomly select 40%/10%/50% data as training, validation and testing sets.
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