This is the face keypoint train code of project face-detection-project

Last update: Nov 27, 2022

Related tags

Overview

face-key-point-pytorch

1. Data structure

The structure of landmarks_jpg is like below:

|--landmarks_jpg
|----AFW
|------AFW_134212_1_0.jpg
|------AFW_134212_1_1.jpg
|----HELEN
|-------HELEN_232194_1_0.jpg
|-------HELEN_232194_1_1.jpg
|----IBUG
|------IBUG_image_003_1_0.jpg
|------IBUG_image_003_1_1.jpg
|----LFPW
|------LFPW_image_test_0001_0.jpg
|------LFPW_image_test_0001_1.jpg

The structure of landmarks_label is like below:

|--landmarks_label
|----AFW
|------AFW_134212_1_0_pts
|------AFW_134212_1_1_pts
|----HELEN
|-------HELEN_232194_1_0_pts
|-------HELEN_232194_1_1_pts
|----IBUG
|------IBUG_image_003_1_0_pts
|------IBUG_image_003_1_1_pts
|----LFPW
|------LFPW_image_test_0001_0_pts
|------LFPW_image_test_0001_1_pts

You can download it by yourself. You can also download the data from the cloud drive:

name	link
landmarks_jpg.zip	https://pan.baidu.com/s/1AJKpa0ac-6ZPWBASiMv87Q code: nujr
landmarks_label.zip	https://pan.baidu.com/s/1wBAZMFkNQS6R6KLkRl6ktw code: zgl0

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\face-keypoint-vgg16-0.pth"
if load_pretrain_model:
    checkpoint = torch.load(model_dir)
    net.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

This is the face keypoint train code of project face-detection-project

Related tags

Overview

face-key-point-pytorch

1. Data structure

2. how to train

3. how to test

Owner

I‘m X

Coded illumination for improved lensless imaging

DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference

Style transfer, deep learning, feature transform

LightNet++: Boosted Light-weighted Networks for Real-time Semantic Segmentation

Semi-supervised Stance Detection of Tweets Via Distant Network Supervision

Complex-Valued Neural Networks (CVNN)Complex-Valued Neural Networks (CVNN)

GAN-based Matrix Factorization for Recommender Systems

PyTorch implementation of 1712.06087 "Zero-Shot" Super-Resolution using Deep Internal Learning

Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs

Official public repository of paper "Intention Adaptive Graph Neural Network for Category-Aware Session-Based Recommendation"

Sound and Cost-effective Fuzzing of Stripped Binaries by Incremental and Stochastic Rewriting

Python library for computer vision labeling tasks. The core functionality is to translate bounding box annotations between different formats-for example, from coco to yolo.

An example of time series augmentation methods with Keras

Lightweight library to build and train neural networks in Theano

This is a project based on ConvNets used to identify whether a road is clean or dirty. We have used MobileNet as our base architecture and the weights are based on imagenet.

Sequence lineage information extracted from RKI sequence data repo

Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch

Compositional Sketch Search

PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet

Source code of our BMVC 2021 paper: AniFormer: Data-driven 3D Animation with Transformer