Let’s Play Lego in a different way!

https://www.lego.com/en-us
Fig.1 The Schema of the LEGO parts information database
Fig. 2 The architecture of our customized model
Fig. 3 Left is the original image for “2357 brick corner 1x2x2 166R”. The right two images are the random samples of modified copies. Parameters for data augmentation: rescale = 1/225, rotation_range = 40, width_shift_range = 0.1, height_shift_range = 0.1, brightness_range = (0, 2), shear_range = 0.2, zoom_range = 0.2, horizontal_flip = True, and fill_mode = “nearest”.
Fig. 4 Training and validation accuracy with ResNet50 on the original data (left) and the augmented data (right).
Fig. 5 Training and validation accuracy with customized model on the original data (left) and the augmented data (right).
  • Prepare more real pictures, a lot more!
  • Increase the size of our customized model by adding more layers or neurons. Or tune the parameters such as the size of the pooling layer, the number of epochs, the learning rate, etc.
  • Try other pre-trained models for the fine-grained image classification, such as TBMSL-Net (Zhang et al., 2020) and DAT (Ngiam, 2018)
  • Create a web page that allows users to upload pictures!

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