MLD-TResNet-L-AAM[448x448]

Reported on 1 benchmark across 1 task · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology1 result

Multi-Label ClassificationonNUS-WIDE
MAP· 2022-09-14
68.3
best: 70.1 (Q2L-CvT(resolution 384, ImageNet-21K pretrained))
Combining Metric Learning and Attention Heads For Accurate and Efficient Multilabel Image Classification arXiv:2209.06585