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SOTA Fine-grained Visual Categorization benchmarks and papers with code | Wizwand
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Fine-grained Visual Categorization
Benchmarks
Dataset Name
SOTA Method
Dataset Name
SOTA Method
Metric
Trend
Results
Last Updated
Stanford Cars (test)
Relational Proxy
Accuracy
96.3
110
4d ago
FGVCAircraft
TRD
Accuracy
95.6
60
4d ago
Stanford Dogs
CPM
Accuracy
97.1
51
4d ago
FGVC
DA-VPT+
Mean Accuracy
91.94
40
4d ago
FGVC (CUB-200-2011, NABirds, Oxford Flowers, Stanford Cars, Stanford Dogs) (test)
RLRR
CUB-200-2011 Accuracy
89.8
32
4d ago
CUB-Birds
S3N
Accuracy
88.5
26
4d ago
Flowers-102
Finedefics-8B
Accuracy
89.9
22
4d ago
CUB
TRD
Accuracy
92.1
20
4d ago
Stanford Cars
TRD
Accuracy
96.35
13
4d ago
FGVC (CUB-200, NABirds, Oxford Flowers, Stanford Dogs, Stanford Cars) 1.0 (test)
SPT-Deep
Mean Accuracy
91.4
12
4d ago
Food-101 (test)
Proposed (CAP)
Accuracy
98.6
11
4d ago
Pets-37
Finedefics-8B
Accuracy
92.2
10
4d ago
Cars-196
ReFine-RFT-CoT
Accuracy
97.1
9
4d ago
Aircrafts-102
ReFine-RFT-CoT
Accuracy
79.3
9
4d ago
iNaturalist
TRD
Accuracy
72.27
7
4d ago
Soy
TRD
Accuracy
52.15
7
4d ago
Cotton
TRD
Accuracy
70.9
7
4d ago
Dog
SR-GNN
Accuracy
97.3
7
4d ago
Car
SR-GNN
Accuracy
96.1
7
4d ago
Oxford-IIIT Pets (test)
Proposed (CAP)
Accuracy
97.3
6
4d ago
CUB-200 (test)
Proposed (CAP)
Accuracy
91.8
6
4d ago
Soy Cultivar (test)
Relational Proxy
Accuracy
51.2
5
4d ago
Cotton Cultivar (test)
Relational Proxy
Accuracy
69.81
5
4d ago
NAB
SR-GNN
Accuracy
91.2
3
4d ago
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