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

Related tags

Deep Learningface-key-point-pytorch
Overview

face-key-point-pytorch

Python Python torch

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
Owner
Iā€˜m X
Iā€˜m X
GitHub Repository
Improving Query Representations for DenseRetrieval with Pseudo Relevance Feedback:A Reproducibility Study.

APR The repo for the paper Improving Query Representations for DenseRetrieval with Pseudo Relevance Feedback:A Reproducibility Study. Environment setu

ielab 8 Nov 26, 2022
Immortal tracker

Immortal_tracker Prerequisite Our code is tested for Python 3.6. To install required liabraries: pip install -r requirements.txt Waymo Open Dataset P

74 Dec 03, 2022
An open-source outlier detection package by Getcontact Data Team

pyfbad The pyfbad library supports anomaly detection projects. An end-to-end anomaly detection application can be written using the source codes of th

Teknasyon Tech 41 Dec 27, 2022
A script depending on VASP output for calculating Fermi-Softness.

Fermi softness calculation for Vienna Ab initio Simulation Package (VASP) Update 1.1.0: Big update: Rewrote the code. Use Bader atomic division instea

qslin 11 Nov 08, 2022
ReLoss - Official implementation for paper "Relational Surrogate Loss Learning" ICLR 2022

Relational Surrogate Loss Learning (ReLoss) Official implementation for paper "R

Tao Huang 31 Nov 22, 2022
Demo for Real-time RGBD-based Extended Body Pose Estimation paper

Real-time RGBD-based Extended Body Pose Estimation This repository is a real-time demo for our paper that was published at WACV 2021 conference The ou

Renat Bashirov 118 Dec 26, 2022
Collection of sports betting AI tools.

sports-betting sports-betting is a collection of tools that makes it easy to create machine learning models for sports betting and evaluate their perf

George Douzas 109 Dec 31, 2022
PyMatting: A Python Library for Alpha Matting

Given an input image and a hand-drawn trimap (top row), alpha matting estimates the alpha channel of a foreground object which can then be composed onto a different background (bottom row).

PyMatting 1.4k Dec 30, 2022
Includes PyTorch -> Keras model porting code for ConvNeXt family of models with fine-tuning and inference notebooks.

ConvNeXt-TF This repository provides TensorFlow / Keras implementations of different ConvNeXt [1] variants. It also provides the TensorFlow / Keras mo

Sayak Paul 87 Dec 06, 2022
Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

Tensor2Tensor Tensor2Tensor, or T2T for short, is a library of deep learning models and datasets designed to make deep learning more accessible and ac

12.9k Jan 09, 2023
Pytorch implementation of our paper LIMUSE: LIGHTWEIGHT MULTI-MODAL SPEAKER EXTRACTION.

LiMuSE Overview Pytorch implementation of our paper LIMUSE: LIGHTWEIGHT MULTI-MODAL SPEAKER EXTRACTION. LiMuSE explores group communication on a multi

Auditory Model and Cognitive Computing Lab 17 Oct 26, 2022
Deep learning model for EEG artifact removal

DeepSeparator Introduction Electroencephalogram (EEG) recordings are often contaminated with artifacts. Various methods have been developed to elimina

23 Dec 21, 2022
YOLOv5 šŸš€ is a family of object detection architectures and models pretrained on the COCO dataset

YOLOv5 šŸš€ is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research int

é˜æꉍ 73 Dec 16, 2022
Distributed Evolutionary Algorithms in Python

DEAP DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data stru

Distributed Evolutionary Algorithms in Python 4.9k Jan 05, 2023
A benchmark for the task of translation suggestion

WeTS: A Benchmark for Translation Suggestion Translation Suggestion (TS), which provides alternatives for specific words or phrases given the entire d

zhyang 55 Dec 24, 2022
Trading Strategies for Freqtrade

Freqtrade Strategies Strategies for Freqtrade, developed primarily in a partnership between @werkkrew and @JimmyNixx from the Freqtrade Discord. Use t

Bryan Chain 242 Jan 07, 2023
Multiple Object Tracking with Yolov5!

Tracking with yolov5 This implementation is for who need to tracking multi-object only with detector. You can easily track mult-object with your well

9 Nov 08, 2022
OneFlow is a performance-centered and open-source deep learning framework.

OneFlow OneFlow is a performance-centered and open-source deep learning framework. Latest News Version 0.5.0 is out! First class support for eager exe

OneFlow 4.2k Jan 07, 2023
We present a regularized self-labeling approach to improve the generalization and robustness properties of fine-tuning.

Overview This repository provides the implementation for the paper "Improved Regularization and Robustness for Fine-tuning in Neural Networks", which

NEU-StatsML-Research 21 Sep 08, 2022
AbelNN: Deep Learning Python module from scratch

AbelNN: Deep Learning Python module from scratch I have implemented several neural networks from scratch using only Numpy. I have designed the module

Abel 2 Apr 12, 2022