Multi-label image recognition on MS-COCO 2017 (val) benchmark leaderboard · SOTA2 Research

Multi-label image recognition on MS-COCO 2017 (val)

89.2CP (Classification Precision)ML-GCN

Evaluation Results

Method	Links
ML-GCN2019.04		89.2	—	64.1	74.6	90.5	66.5	76.7
SRN2019.04		85.2	—	58.8	67.4	87.4	62.5	72.9
ML-GCN2019.04		85.1	83	72	78	85.8	75.4	80.3
ML-GCN2019.04		84.9	—	61.3	71.2	88.8	65.2	75.2
Multi-Evidence2019.04		84.5	—	62.2	70.6	89.1	64.3	74.7
ResNet-1012019.04		84.1	—	59.4	69.7	89.1	62.8	73.6
ResNet-1012019.04		81.6	77.1	65.4	71.2	82.7	69.9	75.8
ML-GCN2019.04		81.1	80.3	70.1	75.2	83.8	74.2	78.7
Multi-Evidence2019.04		80.3	80.2	66.7	72.8	83.9	70.8	76.8
RNN-Attention2019.04		79.1	—	58.7	67.4	84	63	72
ML-ZSL2019.04		74.1	—	64.5	69	—	—	—
Order-Free RNN2019.04		71.6	—	54.8	62.1	74.2	62.2	67.7
CNN-RNN2019.04		66	—	55.6	60.4	69.2	66.4	67.8
CNN-RNN2019.04		—	61.2	—	—	—	—	—
SRN2019.04		—	77.3	—	—	—	—	—