Title: Efficient Domain Adaptation for Real-Time Semantic Segmentation with Lightweight Networks and Discriminators
For "Advanced Machine Learning" course at Politecnico di Torino
Made by:
- Ivan Magistro Contenta
- Yalda Sadat Mobargha
- Luca Sturaro
The repository contains:
- model/: definitions of models and trained models
- STDC-net: model_stages.py and stdcnet.py
- BiSeNet v1: bisenetv1.py
- best_models/ contains trained models on Domain Shift and Domain Adaptation tasks
- Domain Shift
- without data augmentation: p2c_lr_0001_bs_8_notaug_Saved_model_epoch_50.pth
- with data augmentation: p2c_lr_0001_bs_8_aug_Best_model_epoch_35.pth
- Domain Adaptation
- STDC-net: p3_lr_0001_lrD_00001_bs_8_Saved_model_epoch_50.pth
- BiSeNet v1: p4_bisenetv1_domadpt_lightdiscr_BSv1_Best_model_epoch_25.pth
- Domain Shift
- notebook_files/: implementation of training, validation and other techniques
- run_stdc_bisenetv1.ipynb: useful to run different tasks of the code on GPU (Colab)
- cpu_execution.ipynb: useful to run different tasks of the code on CPU (Colab)
- metrics.ipynb: it contains the metrics of different networks and discriminators, but also the outputs of the best models to be compared to the images and ground truths