Fine-Grained Image Classification

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 )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 171–180 of 353 papers

Title	Date	Tasks	Status
PVP: Pre-trained Visual Parameter-Efficient Tuning	Apr 26, 2023	Fine-Grained Image ClassificationImage Classification	—Unverified
Cascading Hierarchical Networks with Multi-task Balanced Loss for Fine-grained hashing	Mar 20, 2023	Data AugmentationFine-Grained Image Classification	CodeCode Available
Semantic Feature Integration network for Fine-grained Visual Classification	Feb 13, 2023	ClassificationFine-Grained Image Classification	—Unverified
How to Use Dropout Correctly on Residual Networks with Batch Normalization	Feb 13, 2023	ClassificationFine-Grained Image Classification	CodeCode Available
LiT Tuned Models for Efficient Species Detection	Feb 12, 2023	Fine-Grained Image Classificationimage-classification	CodeCode Available
On the Ideal Number of Groups for Isometric Gradient Propagation	Feb 7, 2023	Fine-Grained Image ClassificationImage Classification	—Unverified
An Erudite Fine-Grained Visual Classification Model	Jan 1, 2023	ClassificationDisentanglement	—Unverified
Multi-View Active Fine-Grained Visual Recognition	Jan 1, 2023	Fine-Grained Image ClassificationFine-Grained Visual Recognition	CodeCode Available
TransIFC: Invariant Cues-aware Feature Concentration Learning for Efficient Fine-grained Bird Image Classification	Dec 31, 2022	feature selectionFine-Grained Image Classification	—Unverified
Data Augmentation Vision Transformer for Fine-grained Image Classification	Nov 23, 2022	ClassificationData Augmentation	—Unverified

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All datasets Stanford Cars FGVC-Aircraft CUB-200-2011 NABirds Oxford 102 Flowers Stanford Dogs Oxford-IIIT Pets Caltech-101 Food-101 Oxford-IIIT Pet Dataset CompCars Bird-225

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	TResnet-L + PMD	Accuracy	97.3	—	Unverified
2	CMAL-Net	Accuracy	97.1	—	Unverified
3	I2-HOFI	Accuracy	96.92	—	Unverified
4	TResNet-L + ML-Decoder	Accuracy	96.41	—	Unverified
5	DAT	Accuracy	96.2	—	Unverified
6	ALIGN	Accuracy	96.13	—	Unverified
7	SR-GNN	Accuracy	96.1	—	Unverified
8	EffNet-L2 (SAM)	Accuracy	95.96	—	Unverified
9	SaSPA + CAL	Accuracy	95.72	—	Unverified
10	CAP	Accuracy	95.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	I2-HOFI	Accuracy	96.42	—	Unverified
2	SR-GNN	Accuracy	95.4	—	Unverified
3	Inceptionv4	Accuracy	95.11	—	Unverified
4	CAP	Accuracy	94.9	—	Unverified
5	CMAL-Net	Accuracy	94.7	—	Unverified
6	TBMSL-Net	Accuracy	94.7	—	Unverified
7	CSQA-Net	Accuracy	94.7	—	Unverified
8	PART	Accuracy	94.6	—	Unverified
9	AENet	Accuracy	94.5	—	Unverified
10	SaSPA + CAL	Accuracy	94.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	HERBS	Accuracy	93.1	—	Unverified
2	PIM	Accuracy	92.8	—	Unverified
3	MDCM	Accuracy	92.5	—	Unverified
4	SFETrans	Accuracy	91.8	—	Unverified
5	CAP	Accuracy	91.8	—	Unverified
6	IELT	Accuracy	91.8	—	Unverified
7	TransFG	Accuracy	91.7	—	Unverified
8	SWAG (ViT H/14)	Accuracy	91.7	—	Unverified
9	ViT-NeT	Accuracy	91.7	—	Unverified
10	FFVT	Accuracy	91.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	HERBS	Accuracy	93	—	Unverified
2	MetaFormer (MetaFormer-2,384)	Accuracy	93	—	Unverified
3	PIM	Accuracy	92.8	—	Unverified
4	ViT-NeT (SwinV2-B)	Accuracy	92.5	—	Unverified
5	MPSA	Accuracy	92.5	—	Unverified
6	CSQA-Net	Accuracy	92.3	—	Unverified
7	I2-HOFI	Accuracy	92.12	—	Unverified
8	MDCM	Accuracy	92	—	Unverified
9	CGL	Accuracy	91.7	—	Unverified
10	SR-GNN	Accuracy	91.2	—	Unverified