Code for ChaLearn Face Anti-spoofing Attack Detection Challenge @ CVPR2019 by SeuTao
This is the source code for my solution to the ChaLearn Face Anti-spoofing Attack Detection Challenge hosted by ChaLearn.
Recent Update
2019.3.29: Final code is not ready, will update soon.
2019.3.10: code upload for the origanizers to reproduce.
Dependencies
- imgaug==0.2.6
- scikit-image==0.14.0
- scikit-learn==0.19.2
- tqdm==4.23.4
- torch==0.4.1
- torchvision==0.2.1
Train single-modal Model
train model_A with color imgs， patch size 48：
CUDA_VISIBLE_DEVICES=0 python train_CyclicLR.py --model=model_A --image_mode=color --image_size=48
infer
CUDA_VISIBLE_DEVICES=0 python train_CyclicLR.py --mode=infer_test --model=model_A --image_mode=color --image_size=48
Train multi-modal fusion model
train model A fusion model with multi-modal imgs， patch size 48：
CUDA_VISIBLE_DEVICES=0 python train_Fusion_CyclicLR.py --model=model_A --image_size=48
infer
CUDA_VISIBLE_DEVICES=0 python train_Fusion_CyclicLR.py --mode=infer_test --model=model_A --image_size=48
For the origanizers to reproduce final two submissions
unzip the models.zip in the root folder and infer all the trained models
infer
CUDA_VISIBLE_DEVICES=0 python train_CyclicLR.py --mode=infer_test --model=model_A --image_mode=color --image_size=32
CUDA_VISIBLE_DEVICES=0 python train_CyclicLR.py --mode=infer_test --model=model_A --image_mode=depth --image_size=32
CUDA_VISIBLE_DEVICES=0 python train_CyclicLR.py --mode=infer_test --model=model_A --image_mode=ir --image_size=32
CUDA_VISIBLE_DEVICES=0 python train_CyclicLR.py --mode=infer_test --model=model_A --image_mode=color --image_size=48
CUDA_VISIBLE_DEVICES=0 python train_CyclicLR.py --mode=infer_test --model=model_A --image_mode=depth --image_size=48
CUDA_VISIBLE_DEVICES=0 python train_CyclicLR.py --mode=infer_test --model=model_A --image_mode=ir --image_size=48
CUDA_VISIBLE_DEVICES=0 python train_CyclicLR.py --mode=infer_test --model=model_A --image_mode=color --image_size=64
CUDA_VISIBLE_DEVICES=0 python train_CyclicLR.py --mode=infer_test --model=model_A --image_mode=depth --image_size=64
CUDA_VISIBLE_DEVICES=0 python train_CyclicLR.py --mode=infer_test --model=model_A --image_mode=ir --image_size=64
CUDA_VISIBLE_DEVICES=0 python train_Fusion_CyclicLR.py --mode=infer_test --model=baseline --image_size=32
CUDA_VISIBLE_DEVICES=0 python train_Fusion_CyclicLR.py --mode=infer_test --model=baseline --image_size=48
CUDA_VISIBLE_DEVICES=0 python train_Fusion_CyclicLR.py --mode=infer_test --model=baseline --image_size=64
run ensemble script submission.py to generate the final two submissions in phase2: (test_first.txt and test_second.txt)
python submission.py