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Fine-Grained Image Classification datasets
archive 2025-07-2841 datasets carry the task tag "Fine-Grained Image Classification" (the task itself: Fine-Grained Image Classification), ordered by the archive's paper count. Page 1 of 1: 41 shown of 41. Facet routes are this site's own (the archive records the tag string, not a page).
The archive holds 12,214 dataset rows; 12,172 are listed. 6 are withheld from every listing and count here as vandalised before snapshot (6 with contact-centre spam in the title, 0 with a spam description on a row that has no homepage, no paper and no papers counted; none with more than 1 paper, 0 with a benchmark), listed in withheld.json; 1 listed row carries a vandalised description, withheld on its page. This gate never withholds a row with a homepage or a paper that resolves, and a clean description; the content rules below withhold a row whose name is spam whatever else it carries. The gate is a phrase list: these are the rows it caught, not a claim that the rest is clean. Before that gate, the site's content rules withhold 36 more rows (invite-code, gambling, travel-booking, contact-centre and similar spam in the name or on a row with nothing real behind it); they have no page and are listed in withheld.json.
Filter 51 task tags shown of 3,717, by dataset count; the full filter by modality, task and language is on /datasets
All 12,172
Question Answering 413Semantic Segmentation 347Object Detection 332Image Classification 283Classification 222Text Classification 167Language Modelling 166Text Generation 162Visual Question Answering (VQA) 144Named Entity Recognition (NER) 130Pose Estimation 124Anomaly Detection 119Action Recognition 115Instance Segmentation 111Sentiment Analysis 104Text Summarization 98Domain Adaptation 96Speech Recognition 96Reading Comprehension 95Information Retrieval 932D Object Detection 92Image Retrieval 87Machine Translation 82Relation Extraction 822D Semantic Segmentation 81Natural Language Inference 81Image Captioning 79Image Generation 79Depth Estimation 77Node Classification 75Natural Language Understanding 72Autonomous Driving 70Code Generation 70Object Tracking 693D Object Detection 67Face Recognition 67Link Prediction 67Person Re-Identification 65Data Augmentation 63Multi-Task Learning 593D Reconstruction 58Common Sense Reasoning 56Recommendation Systems 56Video Understanding 56Optical Character Recognition (OCR) 55Graph Classification 54Abstractive Text Summarization 52Emotion Recognition 52Medical Image Segmentation 52Word Embeddings 52Fine-Grained Image Classification 41
Fine-Grained Image Classification datasets 1–41 of 41
The MNIST database (Modified National Institute of Standards and Technology database) is a large collection of handwritten digits.
7,651 papers · 44 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.
2,235 papers · 47 benchmarks
Oxford 102 Flower (102 Category Flower Dataset)
Oxford 102 Flower is an image classification dataset consisting of 102 flower categories.
1,307 papers · 16 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.
1,092 papers · 18 benchmarks
The Food-101 dataset consists of 101 food categories with 750 training and 250 test images per category, making a total of 101k images.
805 papers · 14 benchmarks
The Stanford Cars dataset consists of 196 classes of cars with a total of 16,185 images, taken from the rear.
790 papers · 13 benchmarks
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.
709 papers · 10 benchmarks
The iNaturalist 2017 dataset (iNat) contains 675,170 training and validation images from 5,089 natural fine-grained categories.
603 papers · 12 benchmarks
FGVC-Aircraft contains 10,200 images of aircraft, with 100 images for each of 102 different aircraft model variants, most of which are airplanes.
520 papers · 12 benchmarks
EMNIST (Extended MNIST)
EMNIST (extended MNIST) has 4 times more data than MNIST.
264 papers · 10 benchmarks
Stanford Online Products (SOP) dataset has 22,634 classes with 120,053 product images.
231 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.
143 papers · 1 benchmark
Kuzushiji-MNIST is a drop-in replacement for the MNIST dataset (28x28 grayscale, 70,000 images).
97 papers · 2 benchmarks
The Oxford-IIIT Pet Dataset has 37 categories with roughly 200 images for each class.
90 papers · 5 benchmarks
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.
72 papers · 2 benchmarks
CompCars (Comprehensive Cars)
The Comprehensive Cars (CompCars) dataset contains data from two scenarios, including images from web-nature and surveillance-nature.
70 papers · 1 benchmark
The Oxford-IIIT Pet Dataset is a 37-category pet dataset with roughly 200 images for each class.
59 papers · 5 benchmarks
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.
57 papers · 6 benchmarks
The Scene UNderstanding (SUN) database contains 899 categories and 130,519 images.
52 papers · 8 benchmarks
The exact pre-processing steps used to construct the MNIST dataset have long been lost.
26 papers · 2 benchmarks
IP102 contains more than 75,000 images belonging to 102 categories, which exhibit a natural long-tailed distribution.
22 papers · 0 benchmarks
FoodX-251 is a dataset of 251 fine-grained classes with 118k training, 12k validation and 28k test images.
11 papers · 1 benchmark
The CropAndWeed dataset is focused on the fine-grained identification of 74 relevant crop and weed species with a strong emphasis on data variability.
10 papers · 0 benchmarks
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).
7 papers · 0 benchmarks
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.
5 papers · 1 benchmark
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…
3 papers · 0 benchmarks
A new large-scale retail product dataset for fine-grained image classification.
3 papers · 0 benchmarks
DIB-10K (DongNiao International Birds 10000)
Is a challenging image dataset which has more than 10 thousand different types of birds.
2 papers · 1 benchmark
The Herbarium Half-Earth dataset is a large and diverse dataset of herbarium specimens to date for automatic taxon recognition.
2 papers · 1 benchmark
LymphoMNIST is a comprehensive dataset designed for the nuanced classification of lymphocyte images.
2 papers · 0 benchmarks
The Apron Dataset focuses on training and evaluating classification and detection models for airport-apron logistics.
1 paper · 0 benchmarks
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.
1 paper · 0 benchmarks
The development of the remote sensing fine-grained ship classification field necessitates large-scale realistic fine-grained ship datasets.
1 paper · 0 benchmarks
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.
1 paper · 1 benchmark
Orchid2024 is a fine-grained classification dataset specifically designed for Chinese Cymbidium orchid cultivars.
1 paper · 0 benchmarks
SPOT-10 (Animal Pattern Benchmark Dataset for Machine Learning Algorithms)
The SPOTS-10 dataset is an extensive collection of grayscale images showcasing diverse patterns found in ten animal species.
1 paper · 1 benchmark
Tsinghua Dogs is a fine-grained classification dataset for dogs, over 65% of whose images are collected from people's real life.
1 paper · 0 benchmarks
WikiChurches is a dataset for architectural style classification, consisting of 9,485 images of church buildings.
1 paper · 0 benchmarks
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.
1 paper · 0 benchmarks
The iNaturalist Fine-Grained Geolocation dataset is an extension of the iNaturalist dataset with complementary geolocation information.
1 paper · 0 benchmarks
This dataset is the images of corn seeds considering the top and bottom view independently (two images for one corn seed: top and bottom).
0 papers · 0 benchmarks
Paper counts and descriptions are the archive's, frozen 2025-07-28; no citation counts, no stars, no trending. Sorting by "most cited" or "newest" was a live-site feature the archive does not carry.