- Detect if a plate has 1, 2 or 3 letters for the city description :
- Implement a deep learning classifier
- Real plates
- Synthetic plates, taht have been generated to construct a bigger dataset
- Input, example of such a plate:
- A strong preprocessing pipeline to split data into usable training/validation sets and to transform data dimension
- A manually implemented ResNet model, following the original paper
- Redefinition of the residual blocks and stacks in PyTorch
- Use of a classic SGD optimizer (
lr=1e-2, momentum=0.9, weight_decay=1e-2
) - Use of Google GPU capacity, hence the Google Colab notebook.
- High final accuracy (0.999%) on test data