# FeatherNets_Face-Anti-spoofing-Attack-Detection-Challenge-CVPR2019

**Repository Path**: adam1iu/FeatherNets_Face-Anti-spoofing-Attack-Detection-Challenge-CVPR2019

## Basic Information

- **Project Name**: FeatherNets_Face-Anti-spoofing-Attack-Detection-Challenge-CVPR2019
- **Description**: Code for 3rd Place Solution in Face Anti-spoofing Attack Detection Challenge @ CVPR2019,model only 0.35M!!! 1.88ms(CPU)
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-10-10
- **Last Updated**: 2024-06-15

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## FeatherNets for [Face Anti-spoofing Attack Detection Challenge@CVPR2019](https://competitions.codalab.org/competitions/20853#results)[1]

# Params only 0.35M!! FLOPs 80M !!


# Results on the validation set

|model name | ACER|TPR@FPR=10E-2|TPR@FPR=10E-3|FP|FN|epoch|params|FLOPs|
| ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ |
|FishNet150| 0.00144|0.999668|0.998330|19|0|27|24.96M|6452.72M|
|FishNet150| 0.00181|1.0|0.9996|24|0|52|24.96M|6452.72M|
|FishNet150| 0.00496|0.998664|0.990648|48|8|16|24.96M|6452.72M|
|MobileNet v2|0.00228|0.9996|0.9993|28|1|5|2.23M|306.17M
|MobileNet v2|0.00387|0.999433|0.997662|49|1|6|2.23M|306.17M
|MobileNet v2|0.00402|0.9996|0.992623|51|1|7|2.23M|306.17M
|MobileLiteNet54|0.00242|1.0|0.99846|32|0|41|0.57M|270.91M|
|MobileLiteNet54-se|0.00242|1.0|0.996994|32|0|69|0.57M|270.91M|
|FeatherNetA|0.00261|1.00|0.961590|19|7|51|0.35M|79.99M|
|FeatherNetB|0.00168|1.0|0.997662|20|1|48|0.35M|83.05M|
|**Ensembled all**|0.0000|1.0|1.0|0|0|-|-|-|




# Prerequisites

##  install requeirements
```
conda env create -n env_name -f env.yml
```

## Data


### [CASIA-SURF Dataset](https://arxiv.org/abs/1812.00408)[2]


### Our Private Dataset(Available Soon)



### Data index tree
```
├── data
│   ├── our_realsense
│   ├── Training
│   ├── Val
│   ├── Testing
```
Download and unzip our private Dataset into the ./data directory. Then run data/fileList.py to prepare the file list.

### Data Augmentation

| Method | Settings |
| -----  | -------- |
| Random Flip | True |
| Random Crop | 8% ~ 100% |
| Aspect Ratio| 3/4 ~ 4/3 |
| Random PCA Lighting | 0.1 |


# Train the model

### Download pretrained models
download [fishnet150](https://pan.baidu.com/s/1uOEFsBHIdqpDLrbfCZJGUg) pretrained model from [FishNet150 repo](https://github.com/kevin-ssy/FishNet)(Model trained without tricks )

download [mobilenetv2](https://drive.google.com/open?id=1jlto6HRVD3ipNkAl1lNhDbkBp7HylaqR) pretrained model from [MobileNet V2 repo](https://github.com/tonylins/pytorch-mobilenet-v2)

**move them to  ./checkpoints/pre-trainedModels/**


### 1.train FishNet150

> nohup python main.py --config="cfgs/fishnet150-32.yaml" --b 32 --lr 0.01 --every-decay 30 --fl-gamma 2 >> fishnet150-train.log &
###  2.train MobileNet V2

> nohup python main.py --config="cfgs/mobilenetv2.yaml" --b 32 --lr 0.01 --every-decay 40 --fl-gamma 2 >> mobilenetv2-bs32-train.log &

Commands to train the model:
####  3Train MobileLiteNet54
```
python main.py --config="cfgs/MobileLiteNet54-32.yaml" --every-decay 60 -b 32 --lr 0.01 --fl-gamma 3 >>FNet54-bs32-train.log
```
####  4Train MobileLiteNet54-SE
```
python main.py --config="cfgs/MobileLiteNet54-se-64.yaml" --b 64 --lr 0.01  --every-decay 60 --fl-gamma 3 >> FNet54-se-bs64-train.log
```
#### 5Train FeatherNetA
```
python main.py --config="cfgs/FeatherNetA-32.yaml" --b 32 --lr 0.01  --every-decay 60 --fl-gamma 3 >> MobileLiteNetA-bs32-train.log
```
#### 6Train FeatherNetB
```
python main.py --config="cfgs/FeatherNetB-32.yaml" --b 32 --lr 0.01  --every-decay 60 --fl-gamma 3 >> MobileLiteNetB-bs32--train.log

```


## How to create a  submission file
example:
> python main.py --config="cfgs/mobilenetv2.yaml" --resume ./checkpoints/mobilenetv2_bs32/_4_best.pth.tar --val True --val-save True


# Ensemble 

### for validation
```
run EnsembledCode_val.ipynb
```
### for test
```
run EnsembledCode_test.ipynb
```
**notice**:Choose a few models with large differences in prediction results

# Serialized copy of the trained model
You can download my artifacts folder which I used to generate my final submissions: Available Soon

>[1] ChaLearn Face Anti-spoofing Attack Detection Challenge@CVPR2019,[link](https://competitions.codalab.org/competitions/20853?secret_key=ff0e7c30-e244-4681-88e4-9eb5b41dd7f7)

>[2] Shifeng Zhang, Xiaobo Wang, Ajian Liu, Chenxu Zhao, Jun Wan, Sergio Escalera, Hailin Shi, Zezheng Wang, Stan Z. Li, " CASIA-SURF: A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing ", arXiv, 2018 [PDF](https://arxiv.org/abs/1812.00408)