This is the implementation of the paper "SupCon ResNet and Transfer Learning for the In-vehicle Intrusion Detection System"
- tensorflow: 2.0
- torch: 1.9
python3 train_test_split.py --data_path ../Data/ --car_model None --window_size 29 --strided 15 --rid 2
python3 train_baseline.py --data_dir ../Data/TFrecord_w29_s15/ \\
--model resnet18 --save_freq 10 --window_size 29 \\
--num_workers 8 --cosine --epochs 50 \\
--batch_size 256 --learning_rate 0.0005 --rid 5
python3 train_supcon.py --data_dir ../Data/TFrecord_w29_s15/ \\
--model resnet18 --save_freq 10 --window_size 29 \\
--epochs 200 --num_workers 8 --temp 0.07 \\
--learning_rate 0.1 --learning_rate_classifier 0.01 \\
--cosine --epoch_start_classifier 170 --rid 3 --batch_size 512
Random initialization
python3 transfer.py --data_path ../Data/Survival/ --car_model Spark \\
--pretrained_model resnet --tf_algo tune \\
--num_classes 4 --window_size 29 --strided 10 \\
--lr_tune 0.001 --tune_epochs 20
Using CE ResNet as the pretrained model
python3 transfer.py --data_path ../Data/Survival/ --car_model Spark \\
--window_size 29 --strided 10 --num_classes 4 --lr_transfer 0.01 \\
--lr_tune 0.001 --transfer_epochs 50 --tune_epochs 10 \\
--tf_algo transfer_tune --pretrained_model resnet \\
--pretrained_path save/smallresnet18.ce1_gamma0_lr0.001_bs256_50epochs_051822_100142_cosine/models/ \\
--source_ckpt 50
Using SupCon ResNet as the pretrained model
python3 transfer.py --data_path ../Data/Survival/ --car_model Spark \\
--window_size 29 --strided 10 --num_classes 4 --lr_transfer 0.01 \\
--lr_tune 0.001 --transfer_epochs 40 --tune_epochs 20 \\
--tf_algo transfer_tune --pretrained_model supcon \\
--pretrained_path save/SupCon_resnet18.ce2_lr0.05_0.01_bs512_200epoch_temp0.07_052322_102305_cosine_warm/models/ \\
--source_ckpt 200
This codebase was adapted from SupContrast.
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