Fine-Grained Image Classification
173 papers with code • 35 benchmarks • 36 datasets
Fine-Grained Image Classification is a task in computer vision where the goal is to classify images into subcategories within a larger category. For example, classifying different species of birds or different types of flowers. This task is considered to be fine-grained because it requires the model to distinguish between subtle differences in visual appearance and patterns, making it more challenging than regular image classification tasks.
( Image credit: Looking for the Devil in the Details )
Benchmarks
These leaderboards are used to track progress in Fine-Grained Image Classification
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Fine-Grained Image Classification models and implementationsDatasets
MNIST
CUB-200-2011
Oxford 102 Flower
STL-10
Stanford Cars
Food-101
Caltech-101
iNaturalist
FGVC-Aircraft
EMNIST
Most implemented papers
TResNet: High Performance GPU-Dedicated Architecture
rwightman/pytorch-image-models
•
•
In this work, we introduce a series of architecture modifications that aim to boost neural networks' accuracy, while retaining their GPU training and inference efficiency.
Proxy Anchor Loss for Deep Metric Learning
tjddus9597/Proxy-Anchor-CVPR2020
•
•
CVPR 2020
The former class can leverage fine-grained semantic relations between data points, but slows convergence in general due to its high training complexity.
SpinalNet: Deep Neural Network with Gradual Input
dipuk0506/SpinalNet
•
•
arXiv 2020
Traditional learning with ImageNet pre-trained initial weights and SpinalNet classification layers provided the SOTA performance on STL-10, Fruits 360, Bird225, and Caltech-101 datasets.
Roll With the Punches: Expansion and Shrinkage of Soft Label Selection for Semi-supervised Fine-Grained Learning
While semi-supervised learning (SSL) has yielded promising results, the more realistic SSL scenario remains to be explored, in which the unlabeled data exhibits extremely high recognition difficulty, e. g., fine-grained visual classification in the context of SSL (SS-FGVC).
Evaluation of Output Embeddings for Fine-Grained Image Classification
Image classification has advanced significantly in recent years with the availability of large-scale image sets.
Destruction and Construction Learning for Fine-Grained Image Recognition
JDAI-CV/DCL
•
•
CVPR 2019
In this paper, we propose a novel "Destruction and Construction Learning" (DCL) method to enhance the difficulty of fine-grained recognition and exercise the classification model to acquire expert knowledge.
Classification-Specific Parts for Improving Fine-Grained Visual Categorization
Fine-grained visual categorization is a classification task for distinguishing categories with high intra-class and small inter-class variance.
Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization
FlyingMoon-GitHub/ACNet
•
•
CVPR 2020
Specifically, we incorporate convolutional operations along edges of the tree structure, and use the routing functions in each node to determine the root-to-leaf computational paths within the tree.
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features
DreadPiratePsyopus/Fine_Grained_Clf
•
•
Text contained in an image carries high-level semantics that can be exploited to achieve richer image understanding.
Look-into-Object: Self-supervised Structure Modeling for Object Recognition
JDAI-CV/LIO
•
•
CVPR 2020
Specifically, we first propose an object-extent learning module for localizing the object according to the visual patterns shared among the instances in the same category.
