The netowrk used in this work is a VGG based architecture, equipped with a soft proposal layer. It is trained on a database of image-level labelled items. It produces a class-aware soft-proposal map to predict region localisation
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The resulting network is able to achieve ~90% detection accuracy on a test set. Detection and localisation have real-time performance at inference time (40ms per image)
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