Fine-grained 5-way classification on Stanford Cars benchmark leaderboard · SOTA2 Research

Fine-grained 5-way classification on Stanford Cars

92.621-shot AccuracyARF-SFR-Net-Snapshot

Evaluation Results

Method	Links
ARF-SFR-Net-Snapshot2026.04		92.62	98.15
ARF-SFR-Net2026.04		91.24	97.62
BDFRNet2026.04		90.28	97.26
SUITED2026.04		89.9	96.53
FRN+TDM+BiFI-TDM2026.04		89.15	97.31
BSFANet2026.04		88.93	95.2
SRNet2026.04		88.02	96.23
FRN+TDM2026.04		86.91	96.11
FRN2026.04		86.9	95.69
TDM+CSCAM2026.04		86.86	95.63
C2-Net2026.04		86.48	94.07
FRN+CSCAM2026.04		86.24	95.55
ATR-Net2026.04		85.6	95.24
MFSI-Net2026.04		85.6	95.26
ARF-SFR-Net2026.04		85.24	95.59
ARF-SFR-Net-Snapshot2026.04		84.9	95.51
TST_MFL2026.04		82.82	93.61
DeepBDC2026.04		82.28	93.51
SUITED2026.04		82.21	92.39
C2-Net2026.04		79.52	91.15
HelixFormer2026.04		79.4	92.26
BDFRNet2026.04		75.33	90.91
TDM+CSCAM2026.04		73.27	87.81
FRN+TDM2026.04		72.26	89.55
PaCL2026.04		72.21	88.02
FRN+CSCAM2026.04		71.44	86.44
DAN2026.04		70.21	85.55
FRN2026.04		67.48	87.97
ATR-Net2026.04		66.07	82.39
DeepEMD2026.04		61.63	72.95
DN4-DA2019.03		61.51	89.6
LRPABN2026.04		60.28	73.29
DN42019.03		59.84	88.65
GNN+2019.03		55.85	71.25
DSN2026.04		53.45	65.19
BinoHeM2026.04		51.53	70.62
Prototypical Nets+2019.03		40.9	52.93
Matching Nets FCE+2019.03		34.8	44.7
NBNN (Deep local features)2019.03		28.18	38.27
k-NN (Deep global features)2019.03		23.5	34.45