Semi-Supervised Image Classification

The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection. The publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld. ILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”. The ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided. * Total number of non-empty WordNet synsets: 21841 * Total number of images: 14197122 * Number of images with bounding box annotations: 1,034,908 * Number of synsets with SIFT features: 1000 * Number of images with SIFT features: 1.2 million Source: ImageNet Large Scale Visual Recognition Challenge Image Source: https://cs.stanford.edu/people/karpathy/cnnembed/

The CIFAR-100 dataset (Canadian Institute for Advanced Research, 100 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. There are 600 images per class. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs). There are 500 training images and 100 testing images per class. The criteria for deciding whether an image belongs to a class were as follows: * The class name should be high on the list of likely answers to the question “What is in this picture?” * The image should be photo-realistic. Labelers were instructed to reject line drawings. * The image should contain only one prominent instance of the object to which the class refers. * The object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler. Source: https://www.cs.toronto.edu/~kriz/cifar.html Image Source: https://www.cs.toronto.edu/~kriz/cifar.html

Street View House Numbers (SVHN) is a digit classification benchmark dataset that contains 600,000 32×32 RGB images of printed digits (from 0 to 9) cropped from pictures of house number plates. The cropped images are centered in the digit of interest, but nearby digits and other distractors are kept in the image. SVHN has three sets: training, testing sets and an extra set with 530,000 images that are less difficult and can be used for helping with the training process. Source: Reading Digits in Natural Images with Unsupervised Feature Learning Image Source: http://ufldl.stanford.edu/housenumbers/

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. Source: Unsupervised Feature Learning with C-SVDDNet Image Source: https://cs.stanford.edu/~acoates/stl10/

The Caltech101 dataset contains images from 101 object categories (e.g., “helicopter”, “elephant” and “chair” etc.) and a background category that contains the images not from the 101 object categories. For each object category, there are about 40 to 800 images, while most classes have about 50 images. The resolution of the image is roughly about 300×200 pixels. Source: Simple and Efficient Learning using Privileged Information

Caltech-256 is an object recognition dataset containing 30,607 real-world images, of different sizes, spanning 257 classes (256 object classes and an additional clutter class). Each class is represented by at least 80 images. The dataset is a superset of the Caltech-101 dataset. Source: Exploiting Non-Linear Redundancy for Neural Model Compression Image Source: ML4A

Salinas Scene is a hyperspectral dataset collected by the 224-band AVIRIS sensor over Salinas Valley, California, and is characterized by high spatial resolution (3.7-meter pixels). The area covered comprises 512 lines by 217 samples. 20 water absorption bands were discarder: [108-112], [154-167], 224. This image was available only as at-sensor radiance data. It includes vegetables, bare soils, and vineyard fields. Salinas groundtruth contains 16 classes.

This split was introduced in TEMI (BMVC 2023) Adaloglou, Nikolas, Felix Michels, Hamza Kalisch, and Markus Kollmann. "Exploring the Limits of Deep Image Clustering using Pretrained Models." In BMVC. 2023.

Semi-iNat is a challenging dataset for semi-supervised classification with a long-tailed distribution of classes, fine-grained categories, and domain shifts between labeled and unlabeled data. The data is obtained from iNaturalist, a community driven project aimed at collecting observations of biodiversity. The dataset comes with standard training, validation and test sets. The training set consists of: * labeled images from 810 species, where around 10% of the images are labeled. * unlabeled images contains unlabeled images from the same set of classes as the labeled images (in-class), plus the images from a different set of classes as the labeled set (out-of-class). The species are guaranteed to have species at the same phylum level in the labels set. This reflects a common scenario where a coarser taxonomic label of an image can be easily obtained.

TMED is a clinically-motivated benchmark dataset for computer vision and machine learning from limited labeled data. Two overall goals inspired this dataset: 1) We wish to improve timely diagnosis and treatment of aortic stenosis (AS), a common degenerative cardiac valve condition. AS is a particularly important condition where automation holds substantial promise. Automated screening for AS can increase referral and treatment rates for patients with this life threatening condition. 2) We wish to provide an authentic assessment of semi-supervised learning (SSL) methods to the computer vision and ML research community. Especially in medical contexts, labels are often difficult and expensive to acquire. SSL is promising way to combine a small labeled set (images plus expert annotations) with a large, easy-to-acquire unlabeled set (images only). However, most existing benchmark datasets don't represent the challenges of truly uncurated unlabeled sets in a medical context. We hope our data release catalyzes work on methods for effective multi-task SSL. The dataset is available for academic use to any researcher who applies for access on our website and agrees to a standard data use agreement (do not share the data with non-approved users, no commercial use, no attempt to reidentify patients, etc.). ### Dataset contents The TMED dataset contains imagery from 2773 patients and supervised labels for two classification tasks from a small subset of 260 patients (because labels are difficult to acquire). All data is de-identified and approved for release by our IRB. Imagery comes from transthoracic echocardiograms acquired in the course of routine care consistent with American Society of Echocardiography (ASE) guidelines, all obtained from 2015-2020 at Tufts Medical Center. When gathering echocardiogram imagery for each patient, a sonographer manipulates a handheld transducer over the patient’s chest, manually choosing different acquisition angles in order to fully assess the heart’s complex anatomy. This imaging process results in multiple cineloop video clips of the heart depicting various anatomical views. We extract one still image from each available video clip, so each patient study is represented in our dataset as multiple images (typically ~100). Each image is preprocessed to a grayscale 64x64 PNG. Two kinds of labels are available for the labeled subset of patients: * View labels (PLAX/PSAX/Other), indicating which standard anatomical view is shown by the image. Each image in our fully-labeled set is annotated. * Diagnostic labels (no AS, mild/moderate AS, severe AS), indicating the severity of disease. Each patient in our fully-labeled set is annotated. For more information, see our website and our published paper at MLHC '21

As part of an ongoing worldwide effort to comprehend and monitor insect biodiversity, we present the BIOSCAN-5M Insect dataset to the machine learning community. BIOSCAN-5M is a comprehensive dataset containing multi-modal information for over 5 million insect specimens, and it significantly expands existing image-based biological datasets by including taxonomic labels, raw nucleotide barcode sequences, assigned barcode index numbers, geographical information, and specimen size. Every record has both image and DNA data. Each record of the BIOSCAN-5M dataset contains six primary attributes: RGB image DNA barcode sequence Barcode Index Number (BIN) Biological taxonomic classification Geographical information Specimen size

The LIMUC dataset is the largest publicly available labeled ulcerative colitis dataset that compromises 11276 images from 564 patients and 1043 colonoscopy procedures. Three experienced gastroenterologists were involved in the annotation process, and all images are labeled according to the Mayo endoscopic score (MES).

23,000 cropped images of tree bark, for 23 species of trees around Quebec City, Canada. The images were captured at a distance between 20-60 cm away from the trunk. Labels include: individual tree ID, its species, and its DBH (diameter at breast height). Pictures were taken with four different devices: Nexus 5, Samsung Galaxy S5, Samsung Galaxy S7, and a Panasonic Lumix DMC-TS5 camera. The dataset is sufficiently large to train a Deep network such as ResNet for species recognition.

The dataset contains constructed multi-modal features (visual and textual), pseudo-labels (on heritage values and attributes), and graph structures (with temporal, social, and spatial links) constructed using User-Generated Content data collected from Flickr social media platform in three global cities containing UNESCO World Heritage property (Amsterdam, Suzhou, Venice). The motivation of data collection in this project is to provide datasets that could be both directly applicable for ML communities as test-bed, and theoretically informative for heritage and urban scholars to draw conclusions on for planning decision-making.