the search speed. PBNet has only three convolutional layers with batch normalization and max pooling, followed by three fully connected layers. The network is trained by $softmax$ loss.

Experiments
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Implementation Details
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Our network architecture can be divided into three parts: localization network (locNet), recognition network (recNet) and pose estimation network (poseNet). In locNet, the backbone network is a fully convolutional network with 12 3D convolutional layers including 4 residual blocks. Since amino acids have a very small volume, we only have one max pooling layer. We followed previous work [@ren2015faster] to design an anchor mechanism to cover various scales and aspect ratios of amino acids. We use 7 aspect ratios and 3 scales, yielding $k=21$ anchors at each position on the last $conv$ feature maps of the backbone network. We applied a $3\times 3\times 3$ convolutional layer to the $conv$ feature volumes, followed by two sibling $1\times 1\times 1$ convolution layers for classification and bounding box regression, respectively. Each anchor was assigned a binary label depending on whether it has an Intercession-over-Union (IoU) with a ground truth amino acid larger than a threshold 0.8. The recNet also has 4 residual blocks, and three fully connected layers while the 