amino acid detection a very challenging task. To the best of our knowledge, the $A^2$ dataset is the first large-scale benchmark for learning automatic molecular structure determination.

3D Density Volume Annotation
----------------------------

The molecular structures and the corresponding density volumes in the $A^2$ dataset were collected from the RCSB Protein DataBase (PDB) and The Electron Microscopy Data Bank (EMDB). Firstly, we selected the volumes with resolution below 5 angstroms (Å). The PDB and EMDB databases contain some inconsistencies where some volumes do not match the ground-truth structure. We manually removed these problematic volumes. As a quality control step, we collected only the chains without any missing atom or amino acid. Ultimately, the $A^2$ dataset contained 250,000 amino acids in 1713 chains are kept to construct the $A^2$ dataset. Following random selection, we obtained a split of 1250 training and 463 validation chains.

Dataset Statistics
------------------

There are 20 categories of common amino acids and 367,929 pairs of overlapping amino acids in the dataset, which means the dataset is highly dense and challenging for detection. As shown in Fig. \[fig:dense\], the dataset has much denser 3D objects than the KITTI dataset [@Geiger2012CVPR].

![The significant object density difference between the proposed dataset 