Inside this folder models for the trained models are stored. Due to the size of the models, the models can be found on link:
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resnet18-imagenet.pth - A pretrained model of the ResNet-18 architecture. Pretrained on ImageNet database,
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resnet50-imagenet.pth - A pretrained model of the ResNet-50 architecture. Pretrained on ImageNet database,
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resnet101-imagenet.pth - A pretrained model of the ResNet-101 architecture. Pretrained on ImageNet database,
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Weights for the segmentation module trained for semantic segmentation of 150 different classes found in the ADE20K database (directory: Pretrained model on 150 classes):
- encoder_epoch_20.pth - Weights for the encoder after training the model for 20 epochs,
- decoder_epoch_20.pth - Weights for the decoder after training the model for 20 epochs.
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Weights for the segmentation module after transfer learning for semantic segmentation of walls. Transfer learning is done only for the last layer of the decoder architecture (directory: Transfer learning - last layer):
- Output_only_encoder.pth - Weights for the encoder,
- Output_only_decoder.pth - Weights for the decoder.
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Weights for the segmentation module after transfer learning for semantic segmentation of walls. Transfer learning is done for the entire decoder architecture (directory: Transfer learning - entire decoder):
- transfer_encoder.pth - Weights of the encoder,
- transfer_decoder.pth - Weights of the decoder.
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Weights for the segmentation module trained for semantic segmentation of only walls (The training is done on a subset of the ADE20K database) (directory: Without transfer learning):
- best_encoder_epoch_19.pth - Weights of the encoder,
- best_decoder_epoch_19.pth - Weights of the decoder.