A deep learning system for cattle identification using muzzle point images
In our implementation, we have provided Jupyter notebooks which were used to train the model. Each model was implemented in a separate notebook. In addition, the data splitting was implemented on another notebook and a csv was generated for the train and test data. Also, the images all copied to a single folder for easy shuffling, and for compatibility with Pytorch. The training was done using a Google Colab account which enabled the team to have better computational resources. To reimplement the work, any platform with GPUs can be used. However, the paths to the folders must be changed accordingly. A step-by-step guide of the implementation is provided in the code documentation which is as comments and markdowns in the respective Jupyter notebooks.