face-property-detection-pytorch
1. Data structure
The structure of landmarks_jpg is like below:
|--celeba1
|----celeba_face
|------000001.jpg
|------000002.jpg
|------ .....
|------020000.jpg
|----celeba_raw_pic
|------000001.jpg
|------000002.jpg
|------ .....
|------020000.jpg
The celeba_raw_pic is the original picture that we do not make any processing. The celeba_face is the face region of the raw pricture.
You can run the below command to finish the data processing.
python3 create_data.py
This command will use MTCNN model to extract the face region. However, some pictures cannot be extracted by the model. For my test, I can not cut out the face region of the below picture.
# file 000199.jpg cannot detect face
# file 001401.jpg cannot detect face
# file 002214.jpg cannot detect face
# file 002432.jpg cannot detect face
# file 002920.jpg cannot detect face
# file 003928.jpg cannot detect face
# file 003946.jpg cannot detect face
# file 004932.jpg cannot detect face
# file 005283.jpg cannot detect face
# file 006010.jpg cannot detect face
# file 006531.jpg cannot detect face
# file 007726.jpg cannot detect face
# file 008287.jpg cannot detect face
# file 011529.jpg cannot detect face
# file 011793.jpg cannot detect face
# file 013374.jpg cannot detect face
# file 013654.jpg cannot detect face
# file 014999.jpg cannot detect face
# file 016530.jpg cannot detect face
# file 016797.jpg cannot detect face
# file 017282.jpg cannot detect face
# file 017586.jpg cannot detect face
# file 018309.jpg cannot detect face
# file 018599.jpg cannot detect face
# file 018884.jpg cannot detect face
# file 019205.jpg cannot detect face
# file 019377.jpg cannot detect face
So I replace them with 000001.jpg. Also, I revise the label file list_attr_celeba.txt. Replace the issue items with 000001.jpg and I get the list_attr_celeba-face.txt You can use BeyondCompare to diff the changes that I make 
You can download the data from the cloud drive:
| name | link |
|---|---|
| celeba_face.zip | https://pan.baidu.com/s/15nsbvla8eCy_n3EsUMH36Q code：5ipn |
| celeba_raw_pic.zip | https://pan.baidu.com/s/1WM3Zo3zLfKsAFvrDl03suQ code：3q70 |
2. how to train
First, install the third-party package:
pip install -r requirements.txt
Then just simply run the below command:
python3 train.py
if you want to use the pretrained models, you can revise the below code as you need:
load_pretrain_model = False
model_dir=r".\pretrain_models\model-resnet-50-justface-state.ptn"
if load_pretrain_model:
checkpoint = torch.load(model_dir)
model.load_state_dict(checkpoint)
3. how to test
Revise the test file name in predict.py and then run the below command:
python3 predict.py
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