U-Net for Semantic Segmentation

Code For the paper : MDPI | Arxiv
Full Code Implementation (including Knowledge Distillation) available here

Overview

This repo has the code to train and test U-Net for Semantic Segmentation task over images. Contains both conventional as well as Federated Traning using FedAvg algorithm in Flower framework.

Getting Datasets

sh getAllData.sh

Federated Testing commands

Inference Testing

python inference.py --data data/CityScape-Dataset/C1-Vehicle_NoPeople-65 --img data/CityScape-Dataset/C1-Vehicle_NoPeople-65/Image/ulm_000009_000019_leftImg8bit.png --meta data/CityScape-Dataset --checkpoint saved_models/unet_epoch_0_1.67928.pt --ind 0

Federated server

python server.py > server.txt

Federated Clients (CityScape)

python client.py --data data/CityScape-Dataset/Train/C1-Vehicle_NoPeople-65 --test data/CityScape-Dataset/Test --meta data/CityScape-Dataset --num_epochs 50 --loss crossentropy --name client1 > client1.txt

python client.py --data data/CityScape-Dataset/Train/C2-People_NoVehicle-22 --test data/CityScape-Dataset/Test --meta data/CityScape-Dataset --num_epochs 50 --loss crossentropy --name client2 > client2.txt

python client.py --data data/CityScape-Dataset/Train/C3-NoVehicle_NoPeople-11 --test data/CityScape-Dataset/Test --meta data/CityScape-Dataset --num_epochs 50 --loss crossentropy --name client3 > client3.txt

Federated Clients (Chennai)

python client.py --data data/Chennai-Dataset/Train/D1 --meta data/Chennai-Dataset --test data/Chennai-Dataset/Test/T1 --num_epochs 50 --loss crossentropy --name clientCHN1 > clientCHN1.txt

python client.py --data data/Chennai-Dataset/Train/D2 --meta data/Chennai-Dataset --test data/Chennai-Dataset/Test/T2 --num_epochs 50 --loss crossentropy --name clientCHN2 > clientCHN2.txt

python client.py --data data/Chennai-Dataset/Train/D3 --meta data/Chennai-Dataset --test data/Chennai-Dataset/Test/T3 --num_epochs 50 --loss crossentropy --name clientCHN3 > clientCHN3.txt

Unified Testing commands

Unified Testing on CityScape Dataset

python train.py --data data/CityScape-Dataset-Unified/Train --test data/CityScape-Dataset-Unified/Test --meta data/CityScape-Dataset-Unified --num_epochs 50 --loss crossentropy --name UnifiedCSP > UnifiedCSP.txt

Unified Testing on Chennai Dataset

python train.py --data data/Chennai-Dataset-Unified/Train --meta data/Chennai-Dataset-Unified --test data/Chennai-Dataset-Unified/Test --num_epochs 50 --loss crossentropy --name UnifiedCHN > UnifiedCHN.txt

KFold Unified commands

Chennai Dataset

python train_kfold.py --data data/Chennai-Dataset-KFold/ --meta data/Chennai-Dataset-KFold/ --name ChennaiKFold --folds 5 --epochs 10 --batch 1 --loss crossentropy --model Custom_Slim_UNet > UnifiedCHNFolded.txt

Cityscape Dataset

python train_kfold.py --data data/CityScape-Dataset-KFold/ --meta data/CityScape-Dataset-KFold/ --name CityScapeKFold --folds 5 --epochs 10 --batch 1 --loss crossentropy --model Custom_Slim_UNet > UnifiedCSPFolded.txt

KFold Federated commmands

Chennai Dataset

python client_kfold.py --data data/Chennai-Federated-Dataset-KFold/C1 --meta data/Chennai-Federated-Dataset-KFold --folds 5 --epochs 10 --loss crossentropy --batch 1 --model Custom_Slim_UNet --name clientKFoldCHN1 > clientKFoldCHN1.txt

python client_kfold.py --data data/Chennai-Federated-Dataset-KFold/C2 --meta data/Chennai-Federated-Dataset-KFold --folds 5 --epochs 10 --loss crossentropy --batch 1 --model Custom_Slim_UNet --name clientKFoldCHN2 > clientKFoldCHN2.txt

python client_kfold.py --data data/Chennai-Federated-Dataset-KFold/C3 --meta data/Chennai-Federated-Dataset-KFold --folds 5 --epochs 10 --loss crossentropy --batch 1 --model Custom_Slim_UNet --name clientKFoldCHN3 > clientKFoldCHN3.txt

Cityscape Dataset

python client_kfold.py --data data/CityScape-Federated-Dataset-KFold/C1 --meta data/CityScape-Federated-Dataset-KFold --folds 5 --epochs 10 --loss crossentropy --batch 1 --model Custom_Slim_UNet --name clientKFoldCSP1 > clientKFoldCSP1.txt

python client_kfold.py --data data/CityScape-Federated-Dataset-KFold/C2 --meta data/CityScape-Federated-Dataset-KFold --folds 5 --epochs 10 --loss crossentropy --batch 1 --model Custom_Slim_UNet --name clientKFoldCSP2 > clientKFoldCSP2.txt

python client_kfold.py --data data/CityScape-Federated-Dataset-KFold/C3 --meta data/CityScape-Federated-Dataset-KFold --folds 5 --epochs 10 --loss crossentropy --batch 1 --model Custom_Slim_UNet --name clientKFoldCSP3 > clientKFoldCSP3.txt