# CVPR19-Face-Anti-spoofing2

**Repository Path**: rayufo/cvpr19-face-anti-spoofing2

## Basic Information

- **Project Name**: CVPR19-Face-Anti-spoofing2
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-01-23
- **Last Updated**: 2021-11-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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](https://competitions.codalab.org/competitions/20853#learn_the_details) hosted by ChaLearn. 
![image](https://github.com/SeuTao/CVPR19-Face-Anti-spoofing/blob/master/PNG/v1_fusion.png)
## 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
```