68 keypoint annotations for COFW test data

Overview

68 keypoint annotations for COFW test data

This repository contains manually annotated 68 keypoints for COFW test data (original annotation of CFOW dataset contains 29 keypoints). The annotations are stored at "COFW68_Data/test_annotations/". For each image, there is one .mat file which contains the location of the keypoints and their visibilities. Also, a face bounding box for each image is stored at "COFW68_Data/cofw68_test_bboxes.mat". These bounding boxes are calculated using similar detection method which is used for 300-W datasets. So a landmark localization model trained on 300-W datasets can be tested on COFW68 dataset.

Download COFW Images:
 cd	COFW68_Data
 wget http://www.vision.caltech.edu/xpburgos/ICCV13/Data/COFW.zip
 unzip COFW.zip
 mv common/xpburgos/behavior/code/pose/COFW_test.mat .

To visualize the annotations execute "VisualizeAnnotations.m"

Evaluate your method

To evaluate your model, create a text file that contains the landmark localization predictions (and their visibilities). Then specify its address and name in Main.m and execute the Main file. For each image there should be 4 lines in the text file. The first line should contain the image index (in COFW_test.mat). The second, third and fouth lines should contains x, y and occlusion state of each keypoint, respectively.

If you like to share your results with others, please send the file containing landmark localization results to [email protected] and it will be added it to the "Results" directory.

Please cite this paper if you use this benchmark for your research paper:

Golnaz Ghiasi, Charless Fowlkes, "Occlusion Coherence: Detecting and Localizing Occluded Faces", arXiv:1506.08347

Issues, Questions, etc

Please contact "gghiasi @ ics.uci.edu"

A Pytorch reproduction of Range Loss, which is proposed in paper 《Range Loss for Deep Face Recognition with Long-Tailed Training Data》

RangeLoss Pytorch This is a Pytorch reproduction of Range Loss, which is proposed in paper 《Range Loss for Deep Face Recognition with Long-Tailed Trai

Youzhi Gu 7 Nov 27, 2021
This code is an implementation for Singing TTS.

MLP Singer This code is an implementation for Singing TTS. The algorithm is based on the following papers: Tae, J., Kim, H., & Lee, Y. (2021). MLP Sin

Heejo You 22 Dec 23, 2022
Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR)

This is the official implementation of our paper Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR), which has been accepted by WSDM2022.

Yongchun Zhu 81 Dec 29, 2022
AI-generated-characters for Learning and Wellbeing

AI-generated-characters for Learning and Wellbeing Click here for the full project page. This repository contains the source code for the paper AI-gen

MIT Media Lab 214 Jan 01, 2023
The code uses SegFormer for Semantic Segmentation on Drone Dataset.

SegFormer_Segmentation The code uses SegFormer for Semantic Segmentation on Drone Dataset. The details for the SegFormer can be obtained from the foll

Dr. Sander Ali Khowaja 1 May 08, 2022
Pytorch implementation of Learning Rate Dropout.

Learning-Rate-Dropout Pytorch implementation of Learning Rate Dropout. Paper Link: https://arxiv.org/pdf/1912.00144.pdf Train ResNet-34 for Cifar10: r

42 Nov 25, 2022
Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences

Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences This repository is an official PyTorch implementation of Neighbor

DIVE Lab, Texas A&M University 8 Jun 12, 2022
Python lib to talk to pylontech lithium batteries (US2000, US3000, ...) using RS485

python-pylontech Python lib to talk to pylontech lithium batteries (US2000, US3000, ...) using RS485 What is this lib ? This lib is meant to talk to P

Frank 26 Dec 28, 2022
Code for the TIP 2021 Paper "Salient Object Detection with Purificatory Mechanism and Structural Similarity Loss"

PurNet Project for the TIP 2021 Paper "Salient Object Detection with Purificatory Mechanism and Structural Similarity Loss" Abstract Image-based salie

Jinming Su 4 Aug 25, 2022
Implementation of the Transformer variant proposed in "Transformer Quality in Linear Time"

FLASH - Pytorch Implementation of the Transformer variant proposed in the paper Transformer Quality in Linear Time Install $ pip install FLASH-pytorch

Phil Wang 209 Dec 28, 2022
Custom IMDB Dataset is extracted between 2020-2021 and custom distilBERT model is trained for movie success probability prediction

IMDB Success Predictor Project involves Web Scraping custom IMDB data between 2020 and 2021 of 10000 movies and shows sorted by number of votes ,fine

Gautam Diwan 1 Jan 18, 2022
code for the ICLR'22 paper: On Robust Prefix-Tuning for Text Classification

On Robust Prefix-Tuning for Text Classification Prefix-tuning has drawed much attention as it is a parameter-efficient and modular alternative to adap

Zonghan Yang 12 Nov 30, 2022
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models (published in ICLR2018)

Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models Pouya Samangouei*, Maya Kabkab*, Rama Chellappa [*: authors co

Maya Kabkab 212 Dec 07, 2022
Lorien: A Unified Infrastructure for Efficient Deep Learning Workloads Delivery

Lorien: A Unified Infrastructure for Efficient Deep Learning Workloads Delivery Lorien is an infrastructure to massively explore/benchmark the best sc

Amazon Web Services - Labs 45 Dec 12, 2022
Using the provided dataset which includes various book features, in order to predict the price of books, using various proposed methods and models.

Using the provided dataset which includes various book features, in order to predict the price of books, using various proposed methods and models.

Nikolas Petrou 1 Jan 13, 2022
Human Detection - Pedestrian Detection using OpenCV Python

Pedestrian Detection using OpenCV Python Follow us on Instagram for Machine Lear

Hrishikesh Dutta 1 Jan 23, 2022
Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks

MGANs Training & Testing code (torch), pre-trained models and supplementary materials for "Precomputed Real-Time Texture Synthesis with Markovian Gene

290 Nov 15, 2022
Anderson Acceleration for Deep Learning

Anderson Accelerated Deep Learning (AADL) AADL is a Python package that implements the Anderson acceleration to speed-up the training of deep learning

Oak Ridge National Laboratory 7 Nov 24, 2022
A pytorch implementation of the CVPR2021 paper "VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild"

VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild A pytorch implementation of the CVPR2021 paper "VSPW: A Large-scale Dataset for Video

45 Nov 29, 2022
MaRS - a recursive filtering framework that allows for truly modular multi-sensor integration

The Modular and Robust State-Estimation Framework, or short, MaRS, is a recursive filtering framework that allows for truly modular multi-sensor integration

Control of Networked Systems - University of Klagenfurt 143 Dec 29, 2022