programming assignment face recognition

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Join the community, add a new evaluation result row, face recognition.

630 papers with code • 23 benchmarks • 65 datasets

Facial Recognition is the task of making a positive identification of a face in a photo or video image against a pre-existing database of faces. It begins with detection - distinguishing human faces from other objects in the image - and then works on identification of those detected faces.

The state of the art tables for this task are contained mainly in the consistent parts of the task : the face verification and face identification tasks.

( Image credit: Face Verification )

Benchmarks Add a Result

Most implemented papers

Facenet: a unified embedding for face recognition and clustering.

On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99. 63%.

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

Recently, a popular line of research in face recognition is adopting margins in the well-established softmax loss function to maximize class separability.

VGGFace2: A dataset for recognising faces across pose and age

The dataset was collected with three goals in mind: (i) to have both a large number of identities and also a large number of images for each identity; (ii) to cover a large range of pose, age and ethnicity; and (iii) to minimize the label noise.

SphereFace: Deep Hypersphere Embedding for Face Recognition

This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space.

A Light CNN for Deep Face Representation with Noisy Labels

This paper presents a Light CNN framework to learn a compact embedding on the large-scale face data with massive noisy labels.

Learning Face Representation from Scratch

The current situation in the field of face recognition is that data is more important than algorithm.

Circle Loss: A Unified Perspective of Pair Similarity Optimization

This paper provides a pair similarity optimization viewpoint on deep feature learning, aiming to maximize the within-class similarity $s_p$ and minimize the between-class similarity $s_n$.

MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition

In this paper, we design a benchmark task and provide the associated datasets for recognizing face images and link them to corresponding entity keys in a knowledge base.

CosFace: Large Margin Cosine Loss for Deep Face Recognition

The central task of face recognition, including face verification and identification, involves face feature discrimination.

RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition

We propose RepMLP, a multi-layer-perceptron-style neural network building block for image recognition, which is composed of a series of fully-connected (FC) layers.

Deep-Learning-Specialization-Coursera

This repo contains the updated version of all the assignments/labs (done by me) of deep learning specialization on coursera by andrew ng. it includes building various deep learning models from scratch and implementing them for object detection, facial recognition, autonomous driving, neural machine translation, trigger word detection, etc., deep learning specialization coursera [updated version 2021].

Announcement

[!IMPORTANT] Check our latest paper (accepted in ICDAR’23) on Urdu OCR

This repo contains all of the solved assignments of Coursera’s most famous Deep Learning Specialization of 5 courses offered by deeplearning.ai

Instructor: Prof. Andrew Ng

This Specialization was updated in April 2021 to include developments in deep learning and programming frameworks. One of the most major changes was shifting from Tensorflow 1 to Tensorflow 2. Also, new materials were added. However, Most of the old online repositories still don’t have old codes. This repo contains updated versions of the assignments. Happy Learning :)

Programming Assignments

Course 1: Neural Networks and Deep Learning

• W2A1 - Logistic Regression with a Neural Network mindset
• W2A2 - Python Basics with Numpy
• W3A1 - Planar data classification with one hidden layer
• W3A1 - Building your Deep Neural Network: Step by Step¶
• W3A2 - Deep Neural Network for Image Classification: Application

Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

• W1A1 - Initialization
• W1A2 - Regularization
• W1A3 - Gradient Checking
• W2A1 - Optimization Methods
• W3A1 - Introduction to TensorFlow

Course 3: Structuring Machine Learning Projects

• There were no programming assignments in this course. It was completely thoeretical.
• Here is a link to the course

Course 4: Convolutional Neural Networks

• W1A1 - Convolutional Model: step by step
• W1A2 - Convolutional Model: application
• W2A1 - Residual Networks
• W2A2 - Transfer Learning with MobileNet
• W3A1 - Autonomous Driving - Car Detection
• W3A2 - Image Segmentation - U-net
• W4A1 - Face Recognition
• W4A2 - Neural Style transfer

Course 5: Sequence Models

• W1A1 - Building a Recurrent Neural Network - Step by Step
• W1A2 - Character level language model - Dinosaurus land
• W1A3 - Improvise A Jazz Solo with an LSTM Network
• W2A1 - Operations on word vectors
• W2A2 - Emojify
• W3A1 - Neural Machine Translation With Attention
• W3A2 - Trigger Word Detection
• W4A1 - Transformer Network
• W4A2 - Named Entity Recognition - Transformer Application
• W4A3 - Extractive Question Answering - Transformer Application

I’ve uploaded these solutions here, only for being used as a help by those who get stuck somewhere. It may help them to save some time. I strongly recommend everyone to not directly copy any part of the code (from here or anywhere else) while doing the assignments of this specialization. The assignments are fairly easy and one learns a great deal of things upon doing these. Thanks to the deeplearning.ai team for giving this treasure to us.

Connect with me

Name: Abdur Rahman

Institution: Indian Institute of Technology Delhi

Find me on:

Deep-Learning-Specialization

Coursera deep learning specialization, convolutional neural networks.

This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images.

• Understand how to build a convolutional neural network, including recent variations such as residual networks.
• Know how to apply convolutional networks to visual detection and recognition tasks.
• Know to use neural style transfer to generate art.
• Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data.

Week 1: Foundations of Convolutional Neural Networks

Key concepts of week 1.

• Understand the convolution operation
• Understand the pooling operation
• Remember the vocabulary used in convolutional neural network (padding, stride, filter, …)
• Build a convolutional neural network for image multi-class classification

Assignment of Week 1

• Quiz 1: The basics of ConvNets
• Programming Assignment: Convolutional Model: step by step
• Programming Assignment: Convolutional Model: application

Week 2: Deep convolutional models

Key concepts of week 2.

• Understand multiple foundational papers of convolutional neural networks
• Analyze the dimensionality reduction of a volume in a very deep network
• Understand and Implement a Residual network
• Build a deep neural network using Keras
• Implement a skip-connection in your network
• Clone a repository from github and use transfer learning

Assignment of Week 2

• Quiz 2: Deep convolutional models
• Programming Assignment: Residual Networks

Week 3: Convolutional Neural Networks

Key concepts of week 3.

• Understand the challenges of Object Localization, Object Detection and Landmark Finding
• Understand and implement non-max suppression
• Understand and implement intersection over union
• Understand how we label a dataset for an object detection application
• Remember the vocabulary of object detection (landmark, anchor, bounding box, grid, …)

Assignment of Week 3

• Quiz 3: Detection algorithms
• Programming Assignment: Car detection with YOLO

Week 4: Special applications: Face recognition & Neural style transfer

Discover how CNNs can be applied to multiple fields, including art generation and face recognition. Implement your own algorithm to generate art and recognize faces!

Assignment of Week 4

• Quiz 4: Special applications: Face recognition & Neural style transfer
• Programming Assignment: Art generation with Neural Style Transfer
• Programming Assignment: Face Recognition

Course Certificate

Convolutional Neural Networks. Week 4. Programming Assignment: Face Recognition -

I’ve tried to reach out to coursera support but with no response so far.

I am quite sure I should have 100% on all the exercises as my results match what the unit tests expects.

However I get a strange error and incorrect results 66% instead of 100%.

I can’t seem to get this to work or get any feedback on why except for the below error message from Grading logs.

It’s is not clear to me what gives. Perhaps someone could clarify what’s wrong and why unit tests don’t behave as expected.

Code Cell UNQ_C2: Unexpected error (TypeError(“ufunc ‘isfinite’ not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ‘‘safe’’”)) occurred during function check. We expected function verify to return verify test 1 failed. Please check that this function is defined properly.

There appears to be an error in your code for the cell labeled “UNQ_C2”.

Please post a screen capture image of the error message.

Yes. I pasted exactly that error. in the description. Perhaps that’s not clear? The screen shot will be exactly the same message. There is no extra information other than this, which is part of the problem, I guess…

Just passing one set of tests does not prove your code is fully correct. E.g. it is usually possible to write your code in a non-general way that is hard-coded in some fashion to match the tests in the notebook. Or perhaps it doesn’t handle all the possible types of inputs.

The specific error message sounds like you probably have a “divide by zero” problem, but this is the verify function and there aren’t really that many “moving parts” here. It must be that the datatype of one of your variables is incorrect. Actually looking again at this routine to refresh my memory, note that you have two choices: you can implement it all in numpy or you can implement it in TF. But (of course) the point is that you need to be consistent one way or the other. Feeding TF tensors to a numpy call or numpy arrays to a TF call will not end well, although I wouldn’t expect that particular “isfinite” error message in that case.

If those suggestions aren’t helpful, then the better thing is just to look at your code. We can’t do that in a public thread, but I’ll send you a DM about how to proceed with that.

That’s not a screen capture image, which is what I requested.

Well, like I said TMosh: The screenshot has exactly the same error message. The code itself holds my entire solution. I assume you don’t mean I should screen shoot, right? I take you mean screenshot of the error? The screen capture would just have the same info as the text except without the ability for you to copy paste …

Of course hardcoding to pass unit tests not correct is obviously not what I’m doing.

There are 3 parts. The first one specifies to use TF. The second part explicitly tells to use numpy. I have just followed the spec as stated. The division by zero should not occur from what I can see in my code. Unless there is a bug in the grading software? I’m not sure where feeding of numpy calls would occur. Is there is such a flow then it would seem this is done by the testing software and then the spec is logically inconsistent. Since it tells you to use TF in UNQ_C1 and numpy in UNQ_C2. Note UNQ_C1 and UNQ_C3 are correct. it is just the UNQ_C2, “verify(image_path, identity, database, model)” that is messing around with me…

@Christopher_Yamba , thanks for your reply.

To close the loop on the public thread, Christopher’s code was correct, but there was apparently something inconsistent in the runtime state of the notebook. It graded fine when I tried it. So Christopher did this sequence:

and that worked. Note that the “Cell → Run All” is optional there: the grader does not depend on seeing the generated output in the notebook. It only needs to call your functions.

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Deep Learning Specialization 2023 by Andrew Ng on Coursera.

arindam96/deep-learning-specialization-coursera

Folders and files, repository files navigation, deep learning specialization on coursera.

Date Started: 2023-07-23 Date Completed: 2023-10-24 Instructor: Andrew Ng

Introduction

This repo contains all my work for this specialization. All the code base, quiz questions, screenshot, and images, are taken from, unless specified, Deep Learning Specialization on Coursera .

Table of Contents

Course 1 - Neural Networks and Deep Learning

• Quiz Introduction to Deep Learning
• Quiz Neural Network Basics
• Practice Programming Assignment: Python Basics with Numpy
• Programming Assignment: Logistic Regression with a Neural Network Mindset
• Quiz Shallow Neural Networks
• Programming Assignment: Planar Data Classification with One Hidden Layer
• Key Concepts on Deep Neural Networks
• Programming Assignment: Building your Deep Neural Network: Step by Step
• Programming Assignment: Deep Neural Network - Application

Course 2 - Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

• Quiz: Practical aspects of Deep Learning
• Programming Assignment: Initialization
• Programming Assignment: Regularization
• Programming Assignment: Gradient Checking
• Quiz Optimization Algorithms
• Programming Assignment Optimization Methods
• Quiz: Hyperparameter tuning, Batch Normalization, Programming Frameworks
• Programming Assignment: TensorFlow Introduction

Course 3 - Structuring Machine Learning Projects

• Quiz: Bird Recognition in the City of Peacetopia (Case Study)
• Quiz: Autonomous Driving (Case Study)

Course 4 - Convolutional Neural Networks

• Quiz: The Basics of ConvNets
• Programming Assignment: Convolutional Model, Step by Step
• Programming Assignment: Convolution Model Application
• Quiz: Deep Convolutional Models
• Programming Assignment: Residual Networks
• Programming Assignment: Transfer Learning with MobileNet
• Quiz: Detection Algorithms
• Programming Assignment: Car detection with YOLO
• Clear Output Before Submitting (For U-Net Assignment)
• Programming Assignment: Image Segmentation with U-Net
• Quiz: Special Applications: Face Recognition & Neural Style Transfer
• Programming Assignment: Face Recognition
• Programming Assignment: Art Generation with Neural Style Transfer

Course 5 - Sequence Models

• Quiz: Recurrent Neural Networks
• Programming Assignment: Building your Recurrent Neural Network - Step by Step
• Programming Assignment: Dinosaur Island-Character-Level Language Modeling
• Programming Assignment: Jazz Improvisation with LSTM
• Quiz: Natural Language Processing & Word Embeddings
• Programming Assignment: Operations on Word Vectors - Debiasing
• Programming Assignment: Emojify
• Quiz: Sequence Models & Attention Mechanism
• Programming Assignment: Neural Machine Translation
• Programming Assignment: Trigger Word Detection
• Quiz: Transformers
• Programming Assignment: Transformers Architecture with TensorFlow
• Lab: Transformer Pre-processing
• Lab: Transformer Network Application: Named-Entity Recognition
• Lab: Transformer Network Application: Question Answering
• Jupyter Notebook 98.0%
• Python 2.0%