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Multi-label networks with branches are proved to perform well in bothaccuracy and speed, but lacks flexibility in providing dynamic extension ontonew labels due to the low efficiency of re-work on annotating and training. Formulti-label classification task, to cover new labels we need to annotate notonly newly collected images, but also the previous whole dataset to checkpresence of these new labels. Also training on whole re-annotated dataset costsmuch time. In order to recognize new labels more effectively and accurately, wepropose GraftNet, which is a customizable tree-like network with its trunkpretrained with a dynamic graph for generic feature extraction, and branchesseparately trained on sub-datasets with single label to improve accuracy.GraftNet could reduce cost, increase flexibility, and incrementally handle newlabels. Experimental results show that it has good performance on our humanattributes recognition task, which is fine-grained multi-label classification.

GraftNet: An Engineering Implementation of CNN for Fine-grained Multi-label Task