Multi Label Classification On Nus Wide
Metrics
MAP
Results
Performance results of various models on this benchmark
Model Name | MAP | Paper Title | Repository |
|---|---|---|---|
| Q2L-TResL(resoluition 448) | 66.3 | Query2Label: A Simple Transformer Way to Multi-Label Classification | - |
| SRN | 62.0 | Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification | - |
| MS-CMA | 61.4 | Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification | - |
| S-CLs | 60.1 | Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection | - |
| MSRN | 61.5 | Multi-layered Semantic Representation Network for Multi-label Image Classification | - |
| Q2L-CvT(resolution 384, ImageNet-21K pretrained) | 70.1 | Query2Label: A Simple Transformer Way to Multi-Label Classification | - |
| TResNet-L (resolution 448) | 65.2 | Asymmetric Loss For Multi-Label Classification | - |
| Q2L-R101(resolution 448) | 65.0 | Query2Label: A Simple Transformer Way to Multi-Label Classification | - |
| MLD-TResNet-L-AAM[448x448] | 68.30 | Combining Metric Learning and Attention Heads For Accurate and Efficient Multilabel Image Classification | - |
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