This upload contains the neural networks used in the paper "Improving filling level classification with adversarial training". The networks are already pre-trained on the 3 splits (S1, S2, S3) of the C-CCM dataset, using six different training strategies. The networks are implemented in PyTorch. More information regarding the C-CCM dataset can be found here: https://corsmal.eecs.qmul.ac.uk/filling.html The CCM_Filling_Level_Pretrained_Models.zip file contains: 3 folders (S1, S2, S3) that correspond to the different dataset splits Each of S1, S2, S3 folders contains 6 subfolders (ST, AT, ST-FT, ST-AFT, AT-FT, AT-AFT) which correspond to the different training strategies used in the paper. Each of the ST, AT, ..., AT-AFT subfolders contains a PyTorch file named last.t7. This is the PyTorch ResNet-18 model that is trained on the corresponding split (S1/S2/S3) using the corresponding training strategy (ST, AT, ..., AT-AFT). A Python example script for loading the models is also provided (load_model.py).

{"references": ["Modas et al. (2021). Provides the pre-trained models used in the preprint paper \"Improving filling level classification with adversarial training\", arXiv:2102.04057"]}