face property detection pytorch

Last update: Oct 18, 2021

Overview

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.

figure1: raw picture

figure2: face region of raw picture

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

face property detection pytorch

Related tags

Overview

face-property-detection-pytorch

1. Data structure

2. how to train

3. how to test

Owner

i am x

Research using Cirq!

Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

Trying to understand alias-free-gan.

This repository includes the official project for the paper: TransMix: Attend to Mix for Vision Transformers.

Connecting Java/ImgLib2 + Python/NumPy

Training DiffWave using variational method from Variational Diffusion Models.

x-transformers-paddle 2.x version

Lenia - Mathematical Life Forms

This Repo is the official CUDA implementation of ICCV 2019 Oral paper for CARAFE: Content-Aware ReAssembly of FEatures

This repository contains the code for the paper ``Identifiable VAEs via Sparse Decoding''.

Kohei's 5th place solution for xview3 challenge

This is the implementation of GGHL (A General Gaussian Heatmap Labeling for Arbitrary-Oriented Object Detection)

Rethinking Transformer-based Set Prediction for Object Detection

Code for our paper "Graph Pre-training for AMR Parsing and Generation" in ACL2022

Official PyTorch implementation of the preprint paper "Stylized Neural Painting", accepted to CVPR 2021.

The code from the paper Character Transformations for Non-Autoregressive GEC Tagging

PyTorch implementation of U-TAE and PaPs for satellite image time series panoptic segmentation.

The Turing Change Point Detection Benchmark: An Extensive Benchmark Evaluation of Change Point Detection Algorithms on real-world data

Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

Distilled coarse part of LoFTR adapted for compatibility with TensorRT and embedded divices