  Convolutional networks have been widely applied for computer vision system.
Encouraged by these results, a U-Net convolutional network was applied to
recognition of vessels and materials in chemistry lab using the recent
Vector-LabPics dataset, which contains 2187 images of materials within mostly
transparent vessels in a chemistry lab and other general settings, labeled with
13 classes. By optimizing hyperparameters including learning rates and learning
rate decays, 87% accuracy in vessel recognition was achieved. In the case of
relatively small training and test sets (relatively rare materials states, the
number of training set samples less than 500 and the number of test set samples
less than 100), a comprehensive improvement over 18% in IoU and 19% in accuracy
for the best model were achieved. Further improvements may be achievable by
incorporating improved convolutional network structure into our models.
