Fine-Grained Image Classification

The MNIST database (Modified National Institute of Standards and Technology database) is a large collection of handwritten digits. It has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger NIST Special Database 3 (digits written by employees of the United States Census Bureau) and Special Database 1 (digits written by high school students) which contain monochrome images of handwritten digits. The digits have been size-normalized and centered in a fixed-size image. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field. Source: http://yann.lecun.com/exdb/mnist/ Image Source: https://en.wikipedia.org/wiki/MNIST_database#/media/File:MnistExamples.png

The Caltech-UCSD Birds-200-2011 (CUB-200-2011) dataset is the most widely-used dataset for fine-grained visual categorization task. It contains 11,788 images of 200 subcategories belonging to birds, 5,994 for training and 5,794 for testing. Each image has detailed annotations: 1 subcategory label, 15 part locations, 312 binary attributes and 1 bounding box. The textual information comes from Reed et al.. They expand the CUB-200-2011 dataset by collecting fine-grained natural language descriptions. Ten single-sentence descriptions are collected for each image. The natural language descriptions are collected through the Amazon Mechanical Turk (AMT) platform, and are required at least 10 words, without any information of subcategories and actions. Source: Fine-grained Visual-textual Representation Learning Image Source: http://www.vision.caltech.edu/visipedia/CUB-200-2011.html

Oxford 102 Flower is an image classification dataset consisting of 102 flower categories. The flowers chosen to be flower commonly occurring in the United Kingdom. Each class consists of between 40 and 258 images. The images have large scale, pose and light variations. In addition, there are categories that have large variations within the category and several very similar categories.

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 Food-101 dataset consists of 101 food categories with 750 training and 250 test images per category, making a total of 101k images. The labels for the test images have been manually cleaned, while the training set contains some noise. Source: Combining Weakly and Webly Supervised Learning for Classifying Food Images Image Source: https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/

The Stanford Cars dataset consists of 196 classes of cars with a total of 16,185 images, taken from the rear. The data is divided into almost a 50-50 train/test split with 8,144 training images and 8,041 testing images. Categories are typically at the level of Make, Model, Year. The images are 360×240. Source: View Independent Vehicle Make, Model and Color Recognition Using Convolutional Neural Network Image Source: https://ai.stanford.edu/~jkrause/cars/car_dataset.html

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

The iNaturalist 2017 dataset (iNat) contains 675,170 training and validation images from 5,089 natural fine-grained categories. Those categories belong to 13 super-categories including Plantae (Plant), Insecta (Insect), Aves (Bird), Mammalia (Mammal), and so on. The iNat dataset is highly imbalanced with dramatically different number of images per category. For example, the largest super-category “Plantae (Plant)” has 196,613 images from 2,101 categories; whereas the smallest super-category “Protozoa” only has 381 images from 4 categories. Source: Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning Image Source: https://github.com/visipedia/inat_comp/tree/master/2017

FGVC-Aircraft contains 10,200 images of aircraft, with 100 images for each of 102 different aircraft model variants, most of which are airplanes. The (main) aircraft in each image is annotated with a tight bounding box and a hierarchical airplane model label. Aircraft models are organized in a four-levels hierarchy. The four levels, from finer to coarser, are: * Model, e.g. Boeing 737-76J. Since certain models are nearly visually indistinguishable, this level is not used in the evaluation. * Variant, e.g. Boeing 737-700. A variant collapses all the models that are visually indistinguishable into one class. The dataset comprises 102 different variants. * Family, e.g. Boeing 737. The dataset comprises 70 different families. * Manufacturer, e.g. Boeing. The dataset comprises 41 different manufacturers. The data is divided into three equally-sized training, validation and test subsets. Source: https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/ Image Source: Fine-Grained Visual Classification of Aircraft

EMNIST (extended MNIST) has 4 times more data than MNIST. It is a set of handwritten digits with a 28 x 28 format. Source: Domain Discrepancy Measure for Complex Models in Unsupervised Domain Adaptation Image Source: https://arxiv.org/pdf/1803.01900.pdf

Stanford Online Products (SOP) dataset has 22,634 classes with 120,053 product images. The first 11,318 classes (59,551 images) are split for training and the other 11,316 (60,502 images) classes are used for testing Source: Deep Metric Learning with Alternating Projections onto Feasible Sets Image Source: https://cvgl.stanford.edu/projects/lifted_struct/

NABirds V1 is a collection of 48,000 annotated photographs of the 400 species of birds that are commonly observed in North America. More than 100 photographs are available for each species, including separate annotations for males, females and juveniles that comprise 700 visual categories. This dataset is to be used for fine-grained visual categorization experiments. Source: https://dl.allaboutbirds.org/nabirds Image Source: https://openaccess.thecvf.com/content_cvpr_2015/papers/Horn_Building_a_Bird_2015_CVPR_paper.pdf

Kuzushiji-MNIST is a drop-in replacement for the MNIST dataset (28x28 grayscale, 70,000 images). Since MNIST restricts us to 10 classes, the authors chose one character to represent each of the 10 rows of Hiragana when creating Kuzushiji-MNIST. Kuzushiji is a Japanese cursive writing style. Source: Deep Learning for Classical Japanese Literature Image Source: https://github.com/rois-codh/kmnist

The Oxford-IIIT Pet Dataset has 37 categories with roughly 200 images for each class. The images have a large variations in scale, pose and lighting. All images have an associated ground truth annotation of breed, head ROI, and pixel level trimap segmentation. Also available on Academic torrent, Link is here Source: https://www.robots.ox.ac.uk/~vgg/data/pets/ Image Source: https://www.robots.ox.ac.uk/~vgg/data/pets/

Birdsnap is a large bird dataset consisting of 49,829 images from 500 bird species with 47,386 images used for training and 2,443 images used for testing. Source: Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks Image Source: http://thomasberg.org/

The Comprehensive Cars (CompCars) dataset contains data from two scenarios, including images from web-nature and surveillance-nature. The web-nature data contains 163 car makes with 1,716 car models. There are a total of 136,726 images capturing the entire cars and 27,618 images capturing the car parts. The full car images are labeled with bounding boxes and viewpoints. Each car model is labeled with five attributes, including maximum speed, displacement, number of doors, number of seats, and type of car. The surveillance-nature data contains 50,000 car images captured in the front view. The dataset can be used for the tasks of: - Fine-grained classification - Attribute prediction - Car model verification The dataset can be also used for other tasks such as image ranking, multi-task learning, and 3D reconstruction.

The Oxford-IIIT Pet Dataset is a 37-category pet dataset with roughly 200 images for each class. The images have large variations in scale, pose, and lighting. All images have an associated ground truth annotation of breed, head ROI, and pixel-level trimap segmentation.

The Stanford Dogs dataset contains 20,580 images of 120 classes of dogs from around the world, which are divided into 12,000 images for training and 8,580 images for testing. Source: Universal-to-Specific Framework for Complex Action Recognition Image Source: https://www.tensorflow.org/datasets/catalog/stanford_dogs

The Scene UNderstanding (SUN) database contains 899 categories and 130,519 images. There are 397 well-sampled categories to evaluate numerous state-of-the-art algorithms for scene recognition. Image Source: The Selection of Useful Visual Words in Class-Imbalanced Image Classification

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

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

FoodX-251 is a dataset of 251 fine-grained classes with 118k training, 12k validation and 28k test images. Human verified labels are made available for the training and test images. The classes are fine-grained and visually similar, for example, different types of cakes, sandwiches, puddings, soups, and pastas. Source: FoodX-251: A Dataset for Fine-grained Food Classification Image Source: Kaur et al

The CropAndWeed dataset is focused on the fine-grained identification of 74 relevant crop and weed species with a strong emphasis on data variability. Annotations of labeled bounding boxes, semantic masks and stem positions are provided for about 112k instances in more than 8k high-resolution images of both real-world agricultural sites and specifically cultivated outdoor plots of rare weed types. Additionally, each sample is enriched with meta-annotations regarding environmental conditions.

WebFG-496 is a dataset for fine-grained recognition that contains 200 subcategories of the "Bird" (Web-bird), 100 subcategories of the Aircraft" (Web-aircraft), and 196 subcategories of the "Car" (Web-car). It has a total number of 53339 web training images.

Blocksworld Image Reasoning Dataset (BIRD) contains images of wooden blocks in different configurations, and the sequence of moves to rearrange one configuration to the other. Source: Blocksworld Revisited: Learning and Reasoning to Generate Event-Sequences from Image Pairs

The Aircraft Context Dataset, a composition of two inter-compatible large-scale and versatile image datasets focusing on manned aircraft and UAVs, is intended for training and evaluating classification, detection and segmentation models in aerial domains. Additionally, a set of relevant meta-parameters can be used to quantify dataset variability as well as the impact of environmental conditions on model performance.

A new large-scale retail product dataset for fine-grained image classification. Unlike previous datasets focusing on relatively few products, more than 500,000 images of retail products on shelves were collected, belonging to 2000 different products. The dataset aims to advance the research in retail object recognition, which has massive applications such as automatic shelf auditing and image-based product information retrieval. Source: RP2K: A Large-Scale Retail Product Dataset for Fine-Grained Image Classification

Is a challenging image dataset which has more than 10 thousand different types of birds. It was created to enable the study of machine learning and also ornithology research. Source: The DongNiao International Birds 10000 Dataset

The Herbarium Half-Earth dataset is a large and diverse dataset of herbarium specimens to date for automatic taxon recognition. The Herbarium 2021: Half-Earth Challenge dataset includes more than 2.5M images representing nearly 65,000 species from the Americas and Oceania that have been aligned to a standardized plant list. This dataset has a long tail; there are a minimum of 3 images per species. However, some species can be represented by more than 100 images. This dataset only includes vascular land plants which include lycophytes, ferns, gymnosperms, and flowering plants. The extinct forms of lycophytes are the major component of coal deposits, ferns are indicators of ecosystem health, gymnosperms provide major habitats for animals, and flowering plants provide almost all of our crops, vegetables, and fruits.

LymphoMNIST is a comprehensive dataset designed for the nuanced classification of lymphocyte images. It encompasses approximately 80,000 high-resolution 64x64 images, meticulously categorized into three primary classes: B cells, T4 cells, and T8 cells.​ Dataset Characteristics: Size: ~80,000 images​ Resolution: 64x64 pixels​ Classes: B cells, T4 cells, T8 cells​ Format: MNIST-like standardized biomedical imagery​ Modality: Microscopy-based high-resolution cell images​ Motivation and Summary: LymphoMNIST aims to bridge the gap in biomedical image analysis by providing a dataset that is vast in scale and rich in detail. It supports a wide array of research endeavors, from fundamental biological studies to advanced computational model development.​ Potential Use Cases: Medical Research: Studying lymphocyte morphology and characteristics​ Machine Learning & AI: Developing and evaluating image classification models​ AutoML & Benchmarking: Serving as a benchmark dataset for automated model training and performance evaluation​ Educational Purposes: Teaching deep learning concepts in biomedical imaging​ Data Collection Process: The dataset comprises high-resolution images of lymphocytes obtained through microscopy. Each image is standardized to a 64x64 pixel resolution to maintain consistency and facilitate analysis.​ Annotations and Labels: Each image is labeled as one of the three lymphocyte classes: B cells, T4 cells, or T8 cells. The labeling process was conducted by experts in the field to ensure accuracy.

The Apron Dataset focuses on training and evaluating classification and detection models for airport-apron logistics. In addition to bounding boxes and object categories the dataset is enriched with meta parameters to quantify the models’ robustness against environmental influences.

CiNAT Birds 2021 (Cross-View iNaturalist-2021 Birds) dataset contains ground-level images of bird species along with satellite images associated with the geolocation of the ground-level images. In total, there are 413,959 pairs for training and 14,831 pairs for validation and testing. The ground-level images are of varying sizes while the satellite images are of size 256x256. Additionally, the dataset comes with rich metadata for each image - geolocation, date, observer id, taxonomy.

The development of the remote sensing fine-grained ship classification field necessitates large-scale realistic fine-grained ship datasets. The FGSC-23 and FGSCR-42 datasets have been pivotal for practical applications, yet they exhibit limitations when assessing advanced classification methods. Specifically, FGSC-23 categorizes data into relatively broad classes, such as lumping all auxiliary ships into one category, which does not align with the granularity required in real-world scenarios. Additionally, the dataset's limited number of categories fails to satisfy the diverse needs of practical applications. FGSCR-42, while providing finer classifications within some broader categories, still lacks comprehensive subcategory coverage. Moreover, the dataset suffers from an imbalance in sample distribution, with six categories containing fewer than ten samples each, which can hinder effective model training. To overcome these limitations, we introduce the FGSCM-52 dataset, an enhanced version of FGSCR-42, designed to offer a more extensive collection of fine-grained remote sensing ship images. FGSCM-52 includes an additional ten categories and increases the number of samples across existing categories, thereby making it serve as a better testbed for fine-grained ship classification.The dataset features image sizes ranging from 50$\times$50 to 1,600$\times$1,700 pixels. Furthermore, FGSCM-52 is equipped with well-defined hierarchical labels, which facilitate a more nuanced understanding of ship classification.

The Herbarium 2022: Flora of North America is a part of a project of the New York Botanical Garden funded by the National Science Foundation to build tools to identify novel plant species around the world. The dataset strives to represent all known vascular plant taxa in North America, using images gathered from 60 different botanical institutions around the world. In botany, a ‘flora’ is a complete account of the plants found in a geographic region. The dichotomous keys and detailed descriptions of diagnostic morphological features contained within a flora are used by botanists to determine which names to apply to plant specimens. This year's competition dataset aims to encapsulate the flora of North America so that we can test the capability of artificial intelligence to replicate this traditional tool —a crucial first step to harnessing AI’s potential botanical applications. The Herbarium 2022: Flora of North America dataset comprises 1.05 M images of 15,501 vascular plants, constituting more than 90% of the taxa documented in North America. Our dataset is constrained to include only vascular land plants (lycophytes, ferns, gymnosperms, and flowering plants). Our dataset has a long-tail distribution. The number of images per taxon is as few as seven and as many as 100 images. Although more images are available, we capped the maximum number in an attempt to ensure sufficient but manageable training data size for competition participants.

The iNaturalist Fine-Grained Geolocation dataset is an extension of the iNaturalist dataset with complementary geolocation information. Source: https://github.com/visipedia/fg_geo Image Source: https://github.com/visipedia/fg_geo

Orchid2024 is a fine-grained classification dataset specifically designed for Chinese Cymbidium orchid cultivars. It includes data collected from 20 cities across 12 provincial administrative regions in China and encompasses 1,269 cultivars from 8 Chinese Cymbidium orchid species and 6 additional categories, totaling 156,630 images. The dataset covers nearly all common Chinese Cymbidium cultivars currently found in China, with its fine granularity and focus on the real world making it a unique and practical resource for researchers and practitioners.