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36 dataset results for 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.

6,980 PAPERS • 52 BENCHMARKS

CUB-200-2011 (Caltech-UCSD Birds-200-2011)

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.

1,955 PAPERS • 44 BENCHMARKS

Oxford 102 Flower (102 Category Flower Dataset)

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.

1,044 PAPERS • 14 BENCHMARKS

STL-10 (Self-Taught Learning 10)

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.

958 PAPERS • 17 BENCHMARKS

Stanford Cars

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.

645 PAPERS • 10 BENCHMARKS

Food-101

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.

589 PAPERS • 10 BENCHMARKS

Caltech-101

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.

579 PAPERS • 7 BENCHMARKS

iNaturalist

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.

496 PAPERS • 12 BENCHMARKS

FGVC-Aircraft

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:

418 PAPERS • 7 BENCHMARKS

EMNIST (Extended MNIST)

EMNIST (extended MNIST) has 4 times more data than MNIST. It is a set of handwritten digits with a 28 x 28 format.

234 PAPERS • 9 BENCHMARKS

Stanford Online Products

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

221 PAPERS • 5 BENCHMARKS

NABirds (North America Birds)

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.

114 PAPERS • 1 BENCHMARK

Kuzushiji-MNIST

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.

82 PAPERS • 2 BENCHMARKS

Oxford-IIIT Pet Dataset

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.

73 PAPERS • 5 BENCHMARKS

CompCars (Comprehensive Cars)

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.

65 PAPERS • 1 BENCHMARK

Birdsnap

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.

64 PAPERS • 1 BENCHMARK

Stanford Dogs

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.

46 PAPERS • 5 BENCHMARKS

Oxford-IIIT Pets

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.

42 PAPERS • 7 BENCHMARKS

SUN397

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.

34 PAPERS • 5 BENCHMARKS

QMNIST

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.

23 PAPERS • 2 BENCHMARKS

IP102

IP102 contains more than 75,000 images belonging to 102 categories, which exhibit a natural long-tailed distribution.

17 PAPERS • 1 BENCHMARK

FoodX-251

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.

8 PAPERS • 1 BENCHMARK

WebFG-496

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.

5 PAPERS • NO BENCHMARKS YET

BIRD (Blocksworld Image Reasoning Dataset)

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.

4 PAPERS • 1 BENCHMARK

CropAndWeed Dataset

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.

4 PAPERS • NO BENCHMARKS YET

Aircraft Context Dataset

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.

3 PAPERS • NO BENCHMARKS YET

Herbarium 2021 Half–Earth

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.

2 PAPERS • 1 BENCHMARK

RP2K

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.

2 PAPERS • NO BENCHMARKS YET

Apron Dataset

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.

1 PAPER • NO BENCHMARKS YET

CiNAT-Birds-2021 (Cross-View iNaturalist Birds 2021)

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.

1 PAPER • NO BENCHMARKS YET

Herbarium 2022 (Identify plant species of the Americas from herbarium specimens)

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.

1 PAPER • 1 BENCHMARK

Tsinghua Dogs

Tsinghua Dogs is a fine-grained classification dataset for dogs, over 65% of whose images are collected from people's real life. Each dog breed in the dataset contains at least 200 images and a maximum of 7,449 images, basically in proportion to their frequency of occurrence in China, so it significantly increases the diversity for each breed over existing dataset. Furthermore, Tsinghua Dogs annotated bounding boxes of the dog’s whole body and head in each image, which can be used for supervising the training of learning algorithms as well as testing them.

1 PAPER • NO BENCHMARKS YET

WikiChurches

WikiChurches is a dataset for architectural style classification, consisting of 9,485 images of church buildings. Both images and style labels were sourced from Wikipedia. The dataset can serve as a benchmark for various research fields, as it combines numerous real-world challenges: fine-grained distinctions between classes based on subtle visual features, a comparatively small sample size, a highly imbalanced class distribution, a high variance of viewpoints, and a hierarchical organization of labels, where only some images are labeled at the most precise level.

1 PAPER • NO BENCHMARKS YET

YFCC100M Fine-Grained Geolocation

The YFCC100M Fine-Grained Geolocation dataset is a subset of 100 a set of 36,146 YFCC100M images that had Flickr tags that could be identified as corresponding to one of the labels in the iNaturalist 2017 dataset. The 36,146 images that were selected so have the following characteristics: the image must have geolocation available, the image must have at most one iNaturalist label, at most ten examples were retained for each label.

1 PAPER • NO BENCHMARKS YET

iNaturalist Fine-Grained Geolocation

The iNaturalist Fine-Grained Geolocation dataset is an extension of the iNaturalist dataset with complementary geolocation information.

1 PAPER • NO BENCHMARKS YET

Corn Seeds Dataset

This dataset is the images of corn seeds considering the top and bottom view independently (two images for one corn seed: top and bottom). There are four classes of the corn seed (Broken-B, Discolored-D, Silkcut-S, and Pure-P) 17802 images are labeled by the experts at the AdTech Corp. and 26K images were unlabeled out of which 9k images were labeled using the Active Learning (BatchBALD)

0 PAPER • NO BENCHMARKS YET

Datasets

36 dataset results for Fine-Grained Image Classification