Image Classification

Dataset with 28,792 retinal images from the EyePACS dataset, based on a three-level quality grading system (i.e., Good', Usable' and `Reject') for evaluating RIQA methods. Source: Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces

iNat2021 is a large-scale image dataset collected and annotated by community scientists that contains over 2.7M images from 10k different species. To make the dataset more accessible the authors have also created a "mini" training dataset with 50 examples per species for a total of 500K images. Each species has 10 validation images, for a total of 100k validation images. There are a total of 500,000 test images. In addition to its overall scale, the main distinguishing feature of iNat2021 is that it contains at least 152 images in the training set for each species.

Animal Kingdom is a large and diverse dataset that provides multiple annotated tasks to enable a more thorough understanding of natural animal behaviors. The wild animal footage used in the dataset records different times of the day in an extensive range of environments containing variations in backgrounds, viewpoints, illumination and weather conditions. More specifically, the dataset contains 50 hours of annotated videos to localize relevant animal behavior segments in long videos for the video grounding task, 30K video sequences for the fine-grained multi-label action recognition task, and 33K frames for the pose estimation task, which correspond to a diverse range of animals with 850 species across 6 major animal classes.

Object detection benchmark for logo detection. Images are natural scenes. Each image contains multiple objects, and each image has a total of 1 logo. Logo detection & classification labels are provided.

The exact pre-processing steps used to construct the MNIST dataset have long been lost. This leaves us with no reliable way to associate its characters with the ID of the writer and little hope to recover the full MNIST testing set that had 60K images but was never released. The official MNIST testing set only contains 10K randomly sampled images and is often considered too small to provide meaningful confidence intervals. 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. QMNIST is licensed under the BSD-style license. Source: https://github.com/facebookresearch/qmnist Image Source: https://github.com/facebookresearch/qmnist

UrbanCars facilitates multi-shortcut learning under the controlled setting with two shortcuts—background and co-occurring object. The task is classifying the car body type into two categories: urban car and country car. The dataset contains three splits: training, validation, and testing. In the training set, two shortcuts spuriously correlate with the car body type. Both validation and testing sets are balanced, i.e., no spurious correlations. The validation set is used for model selection, and the testing set evaluates the mitigation of two shortcuts.

The Behance Artistic Media dataset (BAM!) is a large-scale dataset of contemporary artwork from Behance, a website containing millions of portfolios from professional and commercial artists. We annotate Behance imagery with rich attribute labels for content, emotions, and artistic media. We believe our Behance Artistic Media dataset will be a good starting point for researchers wishing to study artistic imagery and relevant problems. The dataset consists of: * Automatically-labeled binary attribute scores for over 2.5 million images across 20 attributes each * 393,000 crowdsourced binary attribute labels for individual images * Short image descriptions/captions for 74,000 images from the crowd

The ELEVATER benchmark is a collection of resources for training, evaluating, and analyzing language-image models on image classification and object detection. ELEVATER consists of: - Benchmark: A benchmark suite that consists of 20 image classification datasets and 35 object detection datasets, augmented with external knowledge - Toolkit: An automatic hyper-parameter tuning toolkit; Strong language-augmented efficient model adaptation methods. - Baseline: Pre-trained language-free and language-augmented visual models. - Knowledge: A platform to study the benefit of external knowledge for vision problems. - Evaluation Metrics: Sample-efficiency (zero-, few-, and full-shot) and Parameter-efficiency. - Leaderboard: A public leaderboard to track performance on the benchmark The ultimate goal of ELEVATER is to drive research in the development of language-image models to tackle core computer vision problems in the wild.

ImageNet-W(atermark) is a test set to evaluate models’ reliance on the newly found watermark shortcut in ImageNet, which is used to predict the carton class. ImageNet-W is created by overlaying transparent watermarks on the ImageNet validation set. Two metrics are used to evaluate watermark shortcut reliance: (1) IN-W Gap: the top-1 accuracy drop from ImageNet to ImageNet-W, (2) Carton Gap: carton class accuracy increase from ImageNet to ImageNet-W. Combining ImageNet-W with previous out-of-distribution variants of ImageNet (e.g., Stylized ImageNet, ImageNet-R, ImageNet-9) forms a comprehensive suite of multi-shortcut evaluation on ImageNet.

IP102 contains more than 75,000 images belonging to 102 categories, which exhibit a natural long-tailed distribution. Source: IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition

Our goal is to improve upon the status quo for designing image classification models trained in one domain that perform well on images from another domain. Complementing existing work in robustness testing, we introduce the first test dataset for this purpose which comes from an authentic use case where photographers wanted to learn about the content in their images. We built a new test set using 8,900 images taken by people who are blind for which we collected metadata to indicate the presence versus absence of 200 ImageNet object categories. We call this dataset VizWiz-Classification.

Includes 5,824 fundus images labeled with either positive glaucoma (2,392) or negative glaucoma (3,432). Source: Attention Based Glaucoma Detection: A Large-scale Database and CNN Model

A composite dataset that unifies semantic segmentation datasets from different domains. Source: MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation

ETHOS is a hate speech detection dataset. It is built from YouTube and Reddit comments validated through a crowdsourcing platform. It has two subsets, one for binary classification and the other for multi-label classification. The former contains 998 comments, while the latter contains fine-grained hate-speech annotations for 433 comments. Source: https://arxiv.org/abs/2006.08328

PlantDoc is a dataset for visual plant disease detection. The dataset contains 2,598 data points in total across 13 plant species and up to 17 classes of diseases, involving approximately 300 human hours of effort in annotating internet scraped images. Source: PlantDoc: A Dataset for Visual Plant Disease Detection

Chaoyang dataset contains 1111 normal, 842 serrated, 1404 adenocarcinoma, 664 adenoma, and 705 normal, 321 serrated, 840 adenocarcinoma, 273 adenoma samples for training and testing, respectively. This noisy dataset is constructed in the real scenario. - Details: Colon slides from Chaoyang hospital, the patch size is 512 × 512. We invited 3 professional pathologists to label the patches, respectively. We took the parts of labeled patches with consensus results from 3 pathologists as the testing set. Others we used as the training set. For the samples with inconsistent labeling opinions of the three doctors in the training set (this part accounts for about 40%), we randomly selected the opinions from one of the three doctors. - The original WSIs are scanned at X20 objective magnification.

DeepFish as a benchmark suite with a large-scale dataset to train and test methods for several computer vision tasks. The dataset consists of approximately 40 thousand images collected underwater from 20 habitats in the marine environments of tropical Australia. It contains classification labels as well as point-level and segmentation labels to have a more comprehensive fish analysis benchmark. These labels enable models to learn to automatically monitor fish count, identify their locations, and estimate their sizes. Source: https://github.com/alzayats/DeepFish Image Source: https://github.com/alzayats/DeepFish

Kennedy Space Center is a dataset for the classification of wetland vegetation at the Kennedy Space Center, Florida using hyperspectral imagery. Hyperspectral data were acquired over KSC on March 23, 1996 using JPL's Airborne Visible/Infrared Imaging Spectrometer.

An annotated image memorability dataset to date (with 60,000 labeled images from a diverse array of sources). Source: Understanding and Predicting Image Memorability at a Large Scale

MLRSNet is a a multi-label high spatial resolution remote sensing dataset for semantic scene understanding. It provides different perspectives of the world captured from satellites. That is, it is composed of high spatial resolution optical satellite images. MLRSNet contains 109,161 remote sensing images that are annotated into 46 categories, and the number of sample images in a category varies from 1,500 to 3,000. The images have a fixed size of 256×256 pixels with various pixel resolutions (~10m to 0.1m). Moreover, each image in the dataset is tagged with several of 60 predefined class labels, and the number of labels associated with each image varies from 1 to 13. The dataset can be used for multi-label based image classification, multi-label based image retrieval, and image segmentation. Source: https://github.com/cugbrs/MLRSNet Image Source: Qi et al

See paper: Caldas, Sebastian, et al. "Leaf: A benchmark for federated settings." arXiv preprint arXiv:1812.01097 (2018).

10 classes with 50, 000 training and 5, 000 testing images. Please note that, in ANIMAL10N, noisy labels were injected naturally by human mistakes, where its noise rate was estimated at 8%.

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.

Food2K is a large food recognition dataset with 2,000 categories and over 1 million images. Compared with existing food recognition datasets, Food2K bypasses them in both categories and images by one order of magnitude, and thus establishes a new challenging benchmark to develop advanced models for food visual representation learning. Food2K can be further explored to benefit more food-relevant tasks including emerging and more complex ones (e.g., nutritional understanding of food), and the trained models on Food2K can be expected as backbones to improve the performance of more food-relevant tasks.

Brief Description The Neuromorphic-MNIST (N-MNIST) dataset is a spiking version of the original frame-based MNIST dataset. It consists of the same 60 000 training and 10 000 testing samples as the original MNIST dataset, and is captured at the same visual scale as the original MNIST dataset (28x28 pixels). The N-MNIST dataset was captured by mounting the ATIS sensor on a motorized pan-tilt unit and having the sensor move while it views MNIST examples on an LCD monitor as shown in this video. A full description of the dataset and how it was created can be found in the paper below. Please cite this paper if you make use of the dataset. Orchard, G.; Cohen, G.; Jayawant, A.; and Thakor, N. “Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades", Frontiers in Neuroscience, vol.9, no.437, Oct. 2015

TAU Urban Acoustic Scenes 2019 development dataset consists of 10-seconds audio segments from 10 acoustic scenes: airport, indoor shopping mall, metro station, pedestrian street, public square, street with medium level of traffic, travelling by a tram, travelling by a bus, travelling by an underground metro and urban park. Each acoustic scene has 1440 segments (240 minutes of audio). The dataset contains in total 40 hours of audio. Source: https://zenodo.org/record/2589280 Image Source: http://dcase.community/challenge2019/task-acoustic-scene-classification#citation

A large set of images of cats and dogs. Homepage: https://www.microsoft.com/en-us/download/details.aspx?id=54765 Source code: tfds.image_classification.CatsVsDogs Versions: 4.0.0 (default): New split API (https://tensorflow.org/datasets/splits) Download size: 786.68 MiB Source: https://www.tensorflow.org/datasets/catalog/cats_vs_dogs

Contains 300 scans of 10 people in a wide range of poses together with an evaluation methodology. Source: FAUST: Dataset and Evaluation for 3D Mesh Registration

Bongard-HOI testifies to which extent your few-shot visual learner can quickly induce the true HOI concept from a handful of images and perform reasoning with it. Further, the learner is also expected to transfer the learned few-shot skills to novel HOI concepts compositionally.

Update on 3DIdent, where we introduce six additional object classes (Hare, Dragon, Cow, Armadillo, Horse, and Head), and impose a causal graph over the latent variables. For further details, see Appendix B in the associated paper (https://arxiv.org/abs/2106.04619).

The COCO-MLT is created from MS COCO-2017, containing 1,909 images from 80 classes. The maximum of training number per class is 1,128 and the minimum is 6. We use the test set of COCO2017 with 5,000 for evaluation. The ratio of head, medium, and tail classes is 22:33:25 in COCO-MLT.

HyperKvasir dataset contains 110,079 images and 374 videos where it captures anatomical landmarks and pathological and normal findings. A total of around 1 million images and video frames altogether.

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.

We construct the long-tailed version of VOC from its 2012 train-val set. It contains 1,142 images from 20 classes, with a maximum of 775 images per class and a minimum of 4 images per class. The ratio of head, medium, and tail classes after splitting is 6:6:8. We evaluate the performance on VOC2007 test set with 4952 images.

BCN_20000 is a dataset composed of 19,424 dermoscopic images of skin lesions captured from 2010 to 2016 in the facilities of the Hospital Clínic in Barcelona. The dataset can be used for lesion recognition tasks such as lesion segmentation, lesion detection and lesion classification. Source: https://arxiv.org/abs/1908.02288 Image Source: https://arxiv.org/abs/1908.02288