A Detailed Study of Self Supervised Contrastive Loss and Supervised Contrastive Loss

Claim actually close to 1% improvement on image net data set¹.

Classification accuracy from the paper¹

Architecture wise, its a very simple network resnet 50 having a 128-dimensional head. If you want you can add a few more layers as well.

Architecture and training process from the paper¹

def forward(self, x): 
feat = self.encoder(x) #normalizing the 128 vector is required Code self.encoder = resnet50() self.head = nn.Linear(2048, 128)  
feat = F.normalize(self.head(feat), dim=1) 
return feat

As shown in the figure training is done in two-stage.

• Train using contrastive loss (two variations)
• freeze the learned representations and then learn a classifier on a linear layer using a softmax loss. (From the paper)

The above is pretty self explanatory.

Loss, the main flavor of this paper is understanding the self supervised contrastive loss and supervised contrastive loss.

#programming #data-science

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Create QProgressbar with QThread Real Example | PyQt5 | Python

In this python - PyQt5 tutorial we will learn about How to create a QProgressbar using QThread Real Example | PyQt5 | Python. A progress bar is used to give the user an indication of the progress of an operation and to reassure them that the application is still running.

What is PyQt5 QProgressbar ?

QProgressBar widget consists of horizontal or vertical bar that fills up gradually to indicate the progress of a task. it is often used in applications that involve time consuming operations, such as file uploads or downloads, software installations or any other process that may take a while to complete.

QProgressBar widget can be customized to display different colors, fonts, and sizes. It also provides various properties and methods that allow developers to control its behavior, such as the minimum and maximum values, the current value, and the orientation of the bar.

Overall, the QProgressBar widget is a useful tool for providing visual feedback to users on the progress of a task and can help make applications more user-friendly and intuitive.

These are the imports that we need for example

from PyQt5 import QtGui
from PyQt5.QtWidgets import QApplication, QDialog, QProgressBar, QPushButton, QVBoxLayout
import sys
from PyQt5.QtCore import Qt, QThread, pyqtSignal
import time

This is our thread class and this class extends from QThread, a QThread object manages one thread of control within the program. QThreads begin executing in run(). By default, run() starts the event loop by calling exec() and runs a Qt event loop inside the thread.

class MyThread(QThread):
    # Create a counter thread
    change_value = pyqtSignal(int)
    def run(self):
        cnt = 0
        while cnt < 100:
            cnt+=1
            time.sleep(0.3)
            self.change_value.emit(cnt)

After we create our Window class that extends from QDialog and in that class we add the requirements of our window like title, geometry and icon with QProgresBar and also a QPushButton. also we have used some style and design for our progressbar.

class Window(QDialog):
    def __init__(self):
        super().__init__()
        self.title = "PyQt5 ProgressBar"
        self.top = 200
        self.left = 500
        self.width = 300
        self.height = 100
        self.setWindowIcon(QtGui.QIcon("icon.png"))
        self.setWindowTitle(self.title)
        self.setGeometry(self.left, self.top, self.width, self.height)
        vbox = QVBoxLayout()
        self.progressbar = QProgressBar()
        #self.progressbar.setOrientation(Qt.Vertical)
        self.progressbar.setMaximum(100)
        self.progressbar.setStyleSheet("QProgressBar {border: 2px solid grey;border-radius:8px;padding:1px}"
                                       "QProgressBar::chunk {background:yellow}")
        #qlineargradient(x1: 0, y1: 0.5, x2: 1, y2: 0.5, stop: 0 red, stop: 1 white);
        #self.progressbar.setStyleSheet("QProgressBar::chunk {background: qlineargradient(x1: 0, y1: 0.5, x2: 1, y2: 0.5, stop: 0 red, stop: 1 white); }")
        #self.progressbar.setTextVisible(False)
        vbox.addWidget(self.progressbar)
        self.button = QPushButton("Start Progressbar")
        self.button.clicked.connect(self.startProgressBar)
        self.button.setStyleSheet('background-color:yellow')
        vbox.addWidget(self.button)
        self.setLayout(vbox)
        self.show()

These are the methods that we are going to use for starting and setting the value of the QProgressBar.

  def startProgressBar(self):
        self.thread = MyThread()
        self.thread.change_value.connect(self.setProgressVal)
        self.thread.start()
 
    def setProgressVal(self, val):
        self.progressbar.setValue(val)

Also every PyQt5 application must create an application object. 

App = QApplication(sys.argv)

Finally, we enter the mainloop of the application. The event handling starts from this point. 

window = Window()
sys.exit(App.exec_())

Complete source code for QProgressbar with QThread

from PyQt5 import QtGui
from PyQt5.QtWidgets import QApplication, QDialog, QProgressBar, QPushButton, QVBoxLayout
import sys
from PyQt5.QtCore import Qt, QThread, pyqtSignal
import time
 
 
class MyThread(QThread):
    # Create a counter thread
    change_value = pyqtSignal(int)
    def run(self):
        cnt = 0
        while cnt < 100:
            cnt+=1
            time.sleep(0.3)
            self.change_value.emit(cnt)
class Window(QDialog):
    def __init__(self):
        super().__init__()
        self.title = "PyQt5 ProgressBar"
        self.top = 200
        self.left = 500
        self.width = 300
        self.height = 100
        self.setWindowIcon(QtGui.QIcon("icon.png"))
        self.setWindowTitle(self.title)
        self.setGeometry(self.left, self.top, self.width, self.height)
        vbox = QVBoxLayout()
        self.progressbar = QProgressBar()
        #self.progressbar.setOrientation(Qt.Vertical)
        self.progressbar.setMaximum(100)
        self.progressbar.setStyleSheet("QProgressBar {border: 2px solid grey;border-radius:8px;padding:1px}"
                                       "QProgressBar::chunk {background:yellow}")
        #qlineargradient(x1: 0, y1: 0.5, x2: 1, y2: 0.5, stop: 0 red, stop: 1 white);
        #self.progressbar.setStyleSheet("QProgressBar::
        # chunk {background:
        # qlineargradient(x1: 0, y1: 0.5, x2: 1, y2: 0.5, stop: 0 red, stop: 1 white); }")
        #self.progressbar.setTextVisible(False)
        vbox.addWidget(self.progressbar)
        self.button = QPushButton("Start Progressbar")
        self.button.clicked.connect(self.startProgressBar)
        self.button.setStyleSheet('background-color:yellow')
        vbox.addWidget(self.button)
        self.setLayout(vbox)
        self.show()
 
    def startProgressBar(self):
        self.thread = MyThread()
        self.thread.change_value.connect(self.setProgressVal)
        self.thread.start()
 
    def setProgressVal(self, val):
        self.progressbar.setValue(val)
 
 
 
App = QApplication(sys.argv)
window = Window()
sys.exit(App.exec_())

This will be the result of the code for the PyQt5 QProgressBar.

PyQt5 QProgressbar With QThread Practical Example

Also you can watch the complete video for PyQt5 QProgressbar With QThread Practical Example.

article source at: https://codeloop.org

#python #pyqt5 

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As the cost of education is getting higher rapidly, a lot of students have to give up on their dreams to study abroad. Canada is known as the best country to study abroad for Indian students. You will need approximately between INR 12,50,000 to 19,00,000 a year if you’re an Indian citizen and looking forward to studying in Canada…Read more

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A Detailed Study of Self Supervised Contrastive Loss and Supervised Contrastive Loss

Claim actually close to 1% improvement on image net data set¹.

Classification accuracy from the paper¹

Architecture wise, its a very simple network resnet 50 having a 128-dimensional head. If you want you can add a few more layers as well.

Architecture and training process from the paper¹

def forward(self, x): 
feat = self.encoder(x) #normalizing the 128 vector is required Code self.encoder = resnet50() self.head = nn.Linear(2048, 128)  
feat = F.normalize(self.head(feat), dim=1) 
return feat

As shown in the figure training is done in two-stage.

• Train using contrastive loss (two variations)
• freeze the learned representations and then learn a classifier on a linear layer using a softmax loss. (From the paper)

The above is pretty self explanatory.

Loss, the main flavor of this paper is understanding the self supervised contrastive loss and supervised contrastive loss.

#programming #data-science

A Detailed Study of Self Supervised Contrastive Loss and Supervised Contrastive Loss

Supervised Contrastive Learning paper claims a big deal about supervised learning and cross entropy loss vs supervised contrastive loss for better image representation and classification tasks. Lets go in depth in this paper what is about.

Claim actually close to 1% improvement on image net data set¹.

#data-science #machine-learning #ai #neural-networks #deep-learning

10 Self-Supervised Learning Frameworks & Libraries To Use In 2021

Self-supervised learning is gathering steam, slowly but surely. A relatively new technique, self-supervised learning is nothing but training unlabeled data without human supervision. Yann LeCun described it best: Reinforcement learning is like a cherry on a cake, supervised learning is the icing on the cake, and self-supervised learning is the cake. In self-supervised or unsupervised learning, the system learns to predict part of its input from already existing inputs, he said.

Most tech evangelists liken self-supervised learning models to young children, always curious and learning new information from observation. The latest examples of self-supervision include Facebook’s DINO and ViSSL (Vision library for Self-Supervised Learning); Google’s SimCLR; OpenSelfSup and SfMLearner, etc.

Below, we have curated a list of the most popular self-supervised learning models, frameworks, and libraries.

DINO

DINO, a self-supervised learning vision transformers (ViT), is used to segment unlabelled and random images and videos without supervision. In other words, self DIstillation with NO labels. The model generates high accurate segmentation with self-supervised learning and suitable architecture. Also, DINO requires limited computing resources to train models.

Lightly

Lightly is a computer vision framework for self-supervised learning. It helps in understanding and filtering raw image data and can be applied before any data annotation step. The learned representations can further analyse and visualise datasets, alongside selecting a core set of samples.

s3prl

s3prl is an open-source toolkit that stands for Self-Supervised Speech Pre-training and Representation Learning. Self-supervised speech pre-trained models are called upstream in this toolkit and are used in multiple downstream tasks.

#developers corner #machine learning libraries #self-supervision learning tools #self-supervision libraries