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BoxNet is a deep residual convolutional network for particle picking and dirt masking in cryo-EM data. It's pre-trained on a lot of data and performs rather well.

The easiest way to use it is inside Warp. We are constantly re-training the model on the latest version of the training dataset. To benefit from that, grab the latest model from Models containing a "Mask" suffix are pre-trained to also mask out dirt in micrographs. In case this leads to bad results, try a model without masking.

The training dataset is too large to fit in this repository. It is hosted at The .zip file contains the entire corpus. To use it to re-train the model in Warp, put as many of the TIFF files as you want into the "boxnet2training" folder of your Warp installation.

To submit more data to the training repository, please use this page.

BoxNet can't pick helical filaments yet. We are especially looking for good examples of helical data to implement this feature!

Current list of samples

Access code Sample Synthetic Carbon support VPP Released
EMPIAR-10017 beta-galactosidase
EMPIAR-10077 80S ribosome
EMPIAR-10078 20S proteasome
EMPIAR-10081 HCN1 channel
EMPIAR-10084 Haemoglobin
EMPIAR-10089 TcdA1 in prepore state
EMPIAR-10097 Influenza Hemagglutinin
EMPIAR-10122 Apoferritin
EMPIAR-10153 80S ribosome
EMPIAR-10156 80S ribosome
PDB-1sa0 Tubulin-Colchicine
PDB-2gtl Lumbricus Erythrocruorin
PDB-2wri 70S ribosome
PDB-3j9i 20S proteasome
PDB-4hhb Haemoglobin
PDB-4zor S37P MS2 viral capsid
PDB-5foj Grapevine Fanleaf virus
PDB-5mmi Chloroplast ribosome, large subunit
PDB-5ngm 70S ribosome
PDB-5vy5 Aldolase
PDB-5w3l Rhi virus B14
PDB-5w3s TRPML3 channel
PDB-5xnl Stacked PSII-LHCII supercomplex
PDB-5xwy LbuCas13a-crRNA complex
PDB-5y6p Phycobilisome
PDB-6az1 80S ribosome, small subunit
PDB-6b7n Coronavirus spike protein
PDB-6b44 CRISPR Csy surveillance complex
PDB-6bco TRPM4 channel
PDB-6bcx mTORC1
PDB-6bhu MRP1
RNA Polymerase II complex
RNA Polymerase II complex
RNA Polymerase II complex
viral polymerase
nucleosome complex


BoxNet is being developed by Dimitry Tegunov ( in Patrick Cramer's lab at the Max Planck Institute for Biophysical Chemistry in Göttingen, Germany.


BoxNet implemented in TensorFlow and all current training data




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