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Multi-Label Image Classification 벤치마크

Multi-Label Image Classification on MSCOCO

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mAP

84.8 86.17 87.55 88.92 90.3 2023-02 2026-11 IDA-R101(H) — 84.8 (2023-02-25) IDA-R101(H) 576 — 86.3 (2023-02-25) IDA-SwinL(H) 384 — 90.3 (2023-02-25) IDA-R101(H) — 84.8 (2023-02-25) IDA-R101(H) 576 — 86.3 (2023-02-25) IDA-SwinL(H) 384 — 90.3 (2023-02-25) IDA-R101(H) — 84.8 (2023-02-25) IDA-R101(H) 576 — 86.3 (2023-02-25) IDA-SwinL(H) 384 — 90.3 (2023-02-25)
RankModel mAPmean average precision PaperCodeYear
1 IDA-R101(H) 84.8– Causality Compensated Attention for Contextual Biased Visual Recognition yu-gi-oh-leilei/IDA_2023ICLR 2023
2 IDA-R101(H) 576 86.3– Causality Compensated Attention for Contextual Biased Visual Recognition yu-gi-oh-leilei/IDA_2023ICLR 2023
3 IDA-SwinL(H) 384 90.3– Causality Compensated Attention for Contextual Biased Visual Recognition yu-gi-oh-leilei/IDA_2023ICLR 2023
4 ML-AGCN –86.9 Multi Label Image Classification using Adaptive Graph Convolutional Networks (ML-AGCN) 2022
5 IDA-R101(H) 84.8– Causality Compensated Attention for Contextual Biased Visual Recognition yu-gi-oh-leilei/IDA_2023ICLR 2023
6 IDA-R101(H) 576 86.3– Causality Compensated Attention for Contextual Biased Visual Recognition yu-gi-oh-leilei/IDA_2023ICLR 2023
7 IDA-SwinL(H) 384 90.3– Causality Compensated Attention for Contextual Biased Visual Recognition yu-gi-oh-leilei/IDA_2023ICLR 2023
8 ML-AGCN –86.9 Multi Label Image Classification using Adaptive Graph Convolutional Networks (ML-AGCN) 2022
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