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Fine-Grained Image Classification
Fine-grained image classification is a task in computer vision that aims to categorize images into more specific subcategories. This task requires the model to be able to identify and distinguish subtle visual differences and patterns within the same broad category, making it highly challenging. Its application value lies in improving the accuracy and detail of image recognition, suitable for scenarios such as biological species identification and product classification.
Stanford Cars
CUB-200-2011
MetaFormer
(MetaFormer-2,384)
FGVC Aircraft
Inceptionv4
NABirds
HERBS
CUB-200-2011
TBMSL-Net
Oxford 102 Flowers
AutoFormer-S | 384
Stanford Dogs
MP
Oxford-IIIT Pets
µ2Net+ (ViT-L/16)
Caltech-101
Food-101
CAP
Oxford-IIIT Pet Dataset
CompCars
Resnet50 + PMAL
SUN397
SEER (RegNet10B - linear eval)
Birdsnap
EffNet-L2 (SAM)
Bird-225
WideResNet-101 (Spinal FC)
10 Monkey Species
Fruits-360
VGG-19bn
FoodX-251
CSWin-L
Kuzushiji-MNIST
DIB-10K
MetaFGNet
EMNIST-Letters
VGG-5
STL-10
Pre trained wide-resnet-101
Con-Text
PHOC descriptor + Fisher Vector Encoding
FGVC-Aircraft
EnGraf-Net101 (G=4, H=1)
SOP
Assemble-ResNet-FGVC-50
CarFlag-1532
EMNIST-Digits
VGG-5
MNIST
Vanilla FC layer only
Herbarium 2021 Half–Earth
QMNIST
VGG-5
iNaturalist
TASN
Imbalanced CUB-200-2011
PC-Softmax
BoxCars116K
Bottles
Herbarium 2022
Conviformer-B
CarFlag-563
ResNet101-swp