Machine Learning and Neural Networks - page 39

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People Counter using YOLOv8 and Object Tracking |People Counting (Entering & Leaving)

People Counter using YOLOv8 and Object Tracking |People Counting (Entering & Leaving)

The video explains how to create a people counter using YOLOv8 and object tracking. The process involves detecting objects with unique IDs, finding the center coordinate of the detected object, tracking objects with Deep SORT, and detecting when objects cross specific lines to count the number of people entering and leaving a specific area. The unique IDs are stored in lists to count the people entering and leaving the area, and the counts are graphically displayed with green and red circles. The video also provides code for the project and demonstrates the system's output in real-time.

• 00:00:00 In this section of the video tutorial, the presenter explains how to create a people counter using YOLO V8 and object tracking. The process flow starts with implementing YOLO V8 object detection to detect persons and assign a unique ID to each detected object. Then, the center coordinate of the detected object is found, followed by object tracking using Deep SORT, which assigns a unique ID to each detected object. Afterward, lines are drawn to detect when a person crosses them, and their unique ID is added to a list. Finally, the length of the list is found to get the total count of persons crossing each line.
  
  
• 00:05:00 In this section, the video explains the process of people counting using YOLOv8 and object tracking. The method involves tracking the center coordinate of the object's bounding box and detecting when it crosses a green line. The unique ID of the object is then added to a list, which is used to calculate the total count of objects that have passed through the line. The process is repeated frame by frame, and the count is incremented each time an object passes the line. The video also demonstrates the code used for this project, which includes installing dependencies and using the deep sort object tracking method.
  
  
• 00:10:00 In this section of the video, the presenter downloads the Deep Sort files and opens the predict script to make necessary changes for their project requirements. The presenter removes trails and unnecessary data and creates two lines for up and down counting using the video input. They find the coordinate points for each line, which will allow them to keep track of the number of people who have crossed each line and the total count. The presenter shares their screen to demonstrate how to find the coordinates and emphasizes the importance of focusing on the correct area.
  
  
• 00:15:00 In this section, the speaker explains how to define the coordinates for the line and create an empty list. The line's coordinates are copied from a previous step, and the empty list will store the unique ID of objects that cross it. The script also calculates the width and height of the bounding box and stores the unique ID in the appropriate list depending on whether the person is going up or down. The speaker provides a visual representation of the points and coordinates used in this section.
  
  
• 00:20:00 In this section of the video, the speaker explains the code for the People Counter project using YOLOv8 and Object Tracking. They go through the CX and CY coordinates, which represent the center coordinates of the bounding box, and how they are used to draw a circle and rectangle around the detected object. The speaker also explains how the unique ID and label of each object are added to the rectangle using the cv2.puttext function. Finally, they explain how the unique ID is stored in the empty list when the CX coordinate of the object crosses a certain line, indicating an entry or exit.
  
  
• 00:25:00 In this section, the presenter explains how the unique ID is appended into the total count list for persons going up and persons going down. When the center coordinate passes above or below the line, depending on the count, it will be appended into the respective list. The length of each list represents the number of persons going up and down, which is graphically displayed using two circles where green represents persons going up, and red represents persons going down. The presenter emphasizes defining global total count and total down variables and creating a line using cb2.9. The code makes use of CPU/DPM circle to create the circular representation of the count. The presenter reminds the viewers to make sure the runtime is set as GPU, and the session might take a few seconds to download the necessary files.
  
  
• 00:30:00 In this section, the speaker is demonstrating how to download and run a script that uses YOLOv8 and Object Tracking to count the number of people entering and leaving a specific area in a video. The script divides the video into frames and processes them one by one to track the people's movement and count them as they cross specific lines. The output is an annotated video that shows the number of people entering and leaving the area in real-time. The speaker also showcases the output video and explains how the script successfully counts the people's movement.
  
  
• 00:35:00 In this section, the video tutorial demonstrates the use of YOLOv8 and object tracking to count people entering and leaving a particular area. The system counts individuals crossing a line on the image and displays the count as either an up count or down count depending on the direction of movement. The script file for the system is also shared, and viewers are encouraged to subscribe to the channel and leave a thumbs-up.

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Real-Time Object Detection, Tracking, Blurring and Counting using YOLOv8: A Step-by-Step Tutorial

Real-Time Object Detection, Tracking, Blurring and Counting using YOLOv8: A Step-by-Step Tutorial

This tutorial focuses on implementing object blurring and counting with real-time object detection and tracking using YOLOv8. The tutorial provides steps for downloading the required files, including Deep Sort files for object tracking and a sample video for testing. The tutorial uses OpenCV's CV2 library for blurring the detected objects and provides code for object detection, tracking, and blurring. The speaker demonstrates the process of determining the coordinates of the bounding box, cropping the image, and applying the blur function. Additionally, the presenter explains the code for counting the total number of objects in each frame using a dictionary and demonstrates how the code detects, tracks, and blurs objects while displaying the total count of objects in each frame. Overall, the results are good, and a GitHub repository for the project is provided in the description.

• 00:00:00 In this section of the tutorial, the focus is on blurring the detected objects and counting the number of objects in each frame. The tutorial uses YOLOv8 for object detection with tracking ID and trails. The tutorial provides a step-by-step guide from selecting the GPU runtime to running the scripts for downloading the required files, including Deep Sort files for object tracking, and a sample video for testing. The tutorial also highlights the use of OpenCV's CV2 library for blurring the detected objects and provides code for implementing object detection, tracking, and blurring.
  
  
• 00:05:00 In this section, the video tutorial explains how to blur the objects detected in the real-time object detection and tracking using YOLOv8. To implement this, one needs to know the coordinates of the bounding box, which includes the top left vertex and bottom right vertex, and then blur the object inside this bounding box. The coordinates are stored in the "predict.pi" file, and one can obtain these values for blurring the objects.
  
  
• 00:10:00 In this section of the video, the speaker explains how to blur the object inside the bounding box of the detected object. He first determines the coordinates of the bounding box before cropping the image to only contain the area where the bounding box is located. Then, he applies the blur function using CV2 dot blur and sets the blood ratio, which determines the blurriness of the area. The speaker demonstrates the process by writing and running the script.
  
  
• 00:15:00 In this section of the video, the presenter demonstrates how to implement object blurring using YOLOv8 for real-time object detection, tracking, and counting. After making some minor corrections and running the script, the presenter shows the results of the object blurring feature, which works well. Next, the presenter moves on to explain the code for counting the total number of objects in each frame, which involves creating a dictionary that contains the object name and the number of times it appears in the current frame. The presenter doesn't show the code for this part to avoid making the video too long.
  
  
• 00:20:00 In this section, the presenter explains the count function, which extracts keys and values from a dictionary containing object names and their count (the number of appearances in the current front frame). This function creates a rectangle and overlays it with the text displaying the number of times the object has appeared. The presenter also demonstrates how the code detects and tracks objects, blurs them, and displays the total count of objects in each frame. The results are good, and a GitHub repository for the project is provided in the description.

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Train YOLOv8 on Custom Dataset | Sign Language Alphabets Detection and Recognition using YOLOv8

Train YOLOv8 on Custom Dataset | Sign Language Alphabets Detection and Recognition using YOLOv8

The video demonstrates the implementation of YOLOv8 on a custom dataset for sign language alphabet detection and recognition. The process involves downloading the dataset, training the model for 50 epochs, and evaluating its performance using the confusion matrix and training and validation losses. The presenter also discusses how the model's predictions on the validation batch and images not used for training are validated to determine how it behaves on different images. The trained model is then validated and tested on the validation dataset images, and a demo video inference is shown with good results. Overall, the video highlights the application of YOLOv8 for custom dataset training and object detection.

• 00:00:00 In this section of the video, the presenter introduces the topic of training YOLOv8 on custom data for sign language alphabets detection and recognition. They explain the process step by step and discuss how a sign language alphabet detection and recognition system can be implemented using YOLOv8. The presenter imports various libraries including OS, IPython display, and G-LOP, which are needed for displaying confusion matrices, training and validation losses, and test images. The presenter then shows how to check for GPU access and defines a helper variable for easy navigation between different folders containing dataset images. Finally, they install Ultra Analytics using "pip install" and verify that YOLOv8 is installed and working fine.
  
  
• 00:05:00 In this section, the video demonstrates the process of implementing YOLOv8 on a custom dataset for sign language alphabet detection and recognition. The dataset is downloaded from Roboflow, and the YOLOv8 model is trained on it for 50 epochs. The Confusion Matrix is used to evaluate the model’s performance, identifying how well the model can detect and classify different classes. The results show that the model was able to correctly detect Alphabet A 60% of the time, but sometimes it resulted in incorrect classification or was unable to detect it. Overall, the video highlights the application of YOLOv8 for custom dataset training and object detection.
  
  
• 00:10:00 In this section of the video, the presenter discusses the confusion matrix, which shows how well the model handled different classes, and the training and validation losses, with the important ones being the box logs and classification loss. The model's predictions on the validation batch are also shown, and the images not used for training are validated to determine how the model behaves on different images. The custom model is then validated and tested on the validation dataset images, followed by demo video inference, which shows the model detecting and recognizing sign language alphabets with good results. Finally, viewers are encouraged to test their own models with the video dataset provided.

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YOLOv8 Segmentation with Object Tracking: Step-by-Step Code Implementation | Google Colab | Windows

YOLOv8 Segmentation with Object Tracking: Step-by-Step Code Implementation | Google Colab | Windows

This video tutorial provides a comprehensive guide on how to implement YOLOv8 segmentation with deep sort tracking ID plus trails. The presenter walks the viewers through the process of importing necessary script files, installing dependencies, and setting up the required directory for segmentation and object tracking with deep sort. The tutorial includes a demonstration of object tracking with unique IDs and trails of movement, and a discussion on the GitHub repo that provides one-click solution code for YOLOv8 segmentation and deep sort tracking. The tutorial also introduces a patreon's program with exclusive access to video tutorials that will not be uploaded to the YouTube channel. Overall, the tutorial offers a step-by-step explanation of the code implementation for YOLOv8 segmentation with object tracking.

• 00:00:00 In this section of the video tutorial, viewers are guided on how to implement YOLO V8 segmentation with deep sort tracking ID plus trails. The tutorial provides an end-to-end explanation of the code with a demonstration of the implementation process on Google Colab as well as on Windows and Linux. Viewers are also introduced to a new patreon's program, which offers exclusive access to two to three projects per week, including video tutorials that will not be uploaded to the YouTube channel. The video concludes with a discussion of the GitHub repo, which provides the one-click solution code for YOLO V8 segmentation and deep sort tracking, making it easy to implement on custom datasets or the pre-trained MS Coco dataset.
  
  
• 00:05:00 In this section, the presenter goes through the initial steps involved in implementing YOLOv8 segmentation with object tracking. The first step involves cloning the GitHub repo and importing all necessary script files. Dependencies are installed using the setup.py file, and the required directory is set for performing segmentation and object tracking with deep sort. The presenter then downloads a sample video for testing from Google Drive and demonstrates how object tracking is performed with unique IDs assigned to each object and trails showing each object's movement. The video concludes with an explanation of how to implement YOLOv8 on a Windows system using PyCharm.
  
  
• 00:10:00 In this section, the speaker provides a step-by-step guide to implement YOLOv8 segmentation with object tracking in Google Colab on Windows. The process involves cloning the GitHub repository, setting the current directory as the clone folder, installing all the dependencies, and downloading the deep sort files. The deep sort files are required for implementing object tracking using the deep sort algorithm. These files are downloaded and extracted into the segmented tutorial folder. The speaker also mentions that there are multiple object tracking algorithms available that can be used instead of deep sort.
  
  
• 00:15:00 In this section, the speaker discusses the object tracking algorithm and notes that after testing, they have found that the deep sort algorithm performs the best. They explain that they will be using the SORT algorithm for tracking in the script. The speaker downloads a demo video and shows the code, explaining the YOLOv8-sec BT pretrained model used for object tracking. They also discuss the various models of YOLOv8 and their trade-offs between speed and accuracy. Finally, the speaker explains the predict.5 script and highlights how the bounding box colors are defined for detected objects.
  
  
• 00:20:00 In this section, the speaker demonstrates the function that creates a rectangle around the detected object, assigns a unique ID and label, and creates a bounding box. They explain how the UI touch box function is used to create the bounding box and the above rectangle, while the CV dot two rectangle function creates the rectangle around the detected object. The speaker also shows how the draw Dash boxing function finds the center of the bottom edge of the bounding box to draw trajectories and assigns each object a unique ID. Overall, the speaker provides a step-by-step explanation of the code implementation for YOLOv8 segmentation with object tracking.
  
  
• 00:25:00 In this section, the speaker explains the use of a double-ended queue (DQ) instead of a list to store data. The DQ is used to store values of objects and remove the ID of an object that is not in the current frame. The data is used to draw trails using the CV2 tool line. The segmentation is performed, and the output video shows the trails and detected objects with unique ID assigned. The speaker notes that the script may take longer to run on a CPU but can be run on a GPU with the same steps.

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YOLOv8 | Object Detection | Segmentation | Complete Tutorial Google Colab| Single Click Solution

YOLOv8 | Object Detection | Segmentation | Complete Tutorial Google Colab| Single Click Solution

The video tutorial demonstrates how to implement YOLOv8 using Google Colab for object detection and segmentation. Users are guided through the steps of cloning the GitHub repository, installing packages, configuring directories, and importing demo videos from Google Drive for testing. The user is also shown how to run the YOLOv8 model for object detection on a demo video, how to fix any spacing issues, and how to save and download the output video. The tutorial also covers performing segmentation with YOLOv8 and emphasizes the importance of removing previous compressed files before proceeding. A link to download the notebook file is provided, and viewers are encouraged to ask questions in the comment section.

• 00:00:00 In this section, the presenter discusses the implementation of YOLOv8 using Google Colab for detection and segmentation. The tutorial begins with cloning the YOLOv8 GitHub repository and installing required packages. The presenter demonstrates how to configure the system with the required directories and import demo videos from Google Drive for testing. By following these steps and running the provided cells, users can install and implement YOLOv8 in Google Colab for object detection and segmentation.
  
  
• 00:05:00 In this section, the video tutorial discusses running the YOLOv8 model for object detection on a demo video. The video is imported into the folder and the YOLOv8 GitHub repo is added to the notebook. The 'test 1' video is then tested for detection with the YOLOv8 model, and any spacing issues are fixed before trying the 'test 2' video. The output video is saved to a path and can be downloaded to review the results, but the tutorial also includes a script to show the demo video within Google Colab using HTML and OS libraries.
  
  
• 00:10:00 This excerpt seems to be a continuation of a tutorial on creating an object detection model using YOLOv8 in Google Colab. The speaker is making some adjustments to the code and checking for errors to ensure the path is correct for the output video. They mention an issue with missing RB.free and a spacing issue, which they correct before running the code again. The video shows the output of the object detection model.
  
  
• 00:15:00 In this section, the user performs segmentation using YOLOv8. They copy the necessary code and run it to perform segmentation, obtaining impressive results in the output video. The user emphasizes the importance of removing the previous compressed file before proceeding with the segmentation. They also provide a link to download the notebook file and encourage viewers to ask any questions they may have in the comment section.

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AI Face Emotion Recognition | Identifying Facial Expressions With V7

AI Face Emotion Recognition | Identifying Facial Expressions With V7

The video tutorials discuss the process of using V7 platform to create annotated datasets for AI face emotion recognition. The tutorials cover various aspects of the process, including creating a dataset, annotating images and videos for emotions, training the model, and testing it on sample images and live webcams. The importance of accurate labeling for effective training of AI models is emphasized throughout the tutorials, and the V7 platform's features and multiple models are highlighted. The tutorials provide end-to-end examples of the annotation process for identifying facial expressions using AI.

• 00:00:00 In this section, the YouTuber explains how to use the V7 platform to create a facial expression detector. They go through the steps of creating a data set, uploading both images and videos, and labeling the data set. The V7 platform allows the user to train their model for object detection or segmentation and classification, and test it on sample images, videos, or even on webcam. The YouTuber uses the example of an angry facial expression and uploads images to train the model. They also mention that V7 offers various formats for images and videos, including PNG, JPG, JFIF, MP4, and ABI, among others. Overall, this video provides an end-to-end tutorial on how to implement a facial expression detector using the V7 platform.
  
  
• 00:05:00 In this section, the presenter walks through the process of annotating facial expressions with V7 labs. Using the example of the angry class, the presenter demonstrates how to draw a bounding box around a person's face and then create a class label for angry emotions. The presenter then goes on to annotate all 50 images included in the data set, noting that the process can be replicated for annotating video frames as well. Overall, the presenter emphasizes the importance of accurate labeling for effective training of AI face emotion recognition models.
  
  
• 00:10:00 In this section of the video, the presenter shows how to annotate frames from a video dataset to create visual images for AI emotion recognition. The presenter uploads a video dataset and extracts one frame per second to create individual images. The images are then annotated separately to identify the emotion depicted. The presenter notes that the process is time-consuming, but important for creating a comprehensive dataset for machine learning. The presenter also demonstrates the ability to annotate both images and videos for AI emotion recognition. Overall, this section provides a helpful tutorial for creating annotated datasets for AI emotion recognition.
  
  
• 00:15:00 In this section, the speaker explains the process of uploading and annotating a dataset for the "happy" class in AI face emotion recognition using V7 technology. The speaker uploads 50 images and annotates them one by one with the "happy" label. They mention that a total of 182 images have been annotated so far, including 100 for the "angry" class. They also discuss uploading a video and splitting it into separate frames to annotate each one for facial expressions.
  
  
• 00:20:00 In this section, the video tutorial demonstrates the annotation process for identifying happy and fearful individuals in videos and images using AI face emotion recognition. The annotator uploads 66 happy person videos and annotates them with Envision, rotating through each frame and labeling happy or neutral expressions. Next, the annotator adds a new class for fear and uploads 50 images, then annotates each image with the appropriate emotion label. The completed data set contains 248 images and videos, and the tutorial provides an end-to-end example of the annotation process for identifying facial expressions using AI.
  
  
• 00:25:00 In this section, the YouTuber discusses their progress in annotating and animating images for their AI face emotion recognition project. They have successfully annotated all 50 images of the clear glass, and have also completed frame by frame annotation of a fear person video. The YouTuber then proceeds to annotate all the images for the surprise class, which is their last class, and mentions that they will move towards the training part of the video after completing all annotations. The video shows a workflow diagram for the project, and the YouTuber clarifies that they will be doing object detection and creating bounding boxes rather than instant segmentation or classification.
  
  
• 00:30:00 In this section, the video tutorial demonstrates the process of training an AI model on a dataset of facial expressions and emotions using V7. The tutorial shows how to schedule and monitor the training of the dataset, with an email notification sent after completion. The video also highlights the model's performance metrics, including average precision and recall, as well as losses, which continuously decreased over time. The tutorial concludes by showing how the trained model can be deployed for use in various APIs using Python, shell, JavaScript, or Elixir, and how the model can also be tested on live webcams.
  
  
• 00:35:00 In this section, the speaker demonstrates AI face emotion recognition using V7 labs. The process involves gathering and labeling image data sets for the emotions of angry, happy, fear, surprise, and more. The speaker trains the model and tests it using a webcam and samples of images, achieving fine results. V7 labs also offers multiple models for tasks like segmentation and text scanning, and users can create their own models with free credits. The speaker teases future projects using V7 and encourages viewers to share and like the video.

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Real Time Football Player and Ball Detection and Tracking using YOLOv8 Live :Object Tracking YOLOv8

Real Time Football Player and Ball Detection and Tracking using YOLOv8 Live :Object Tracking YOLOv8

In this YouTube video tutorial, the presenter demonstrates the process of creating a football player and ball detection and tracking dataset using Roboflow. The presenter walks through the steps of uploading and annotating images, preparing the dataset, training the model, testing on sample videos and live webcam, and modifying the code to improve tracking. Overall, the YOLOv8 model works well but has some limitations with detecting football in certain scenarios.

• 00:00:00 In this section of the tutorial, the presenter walks through the process of creating a football player and ball detection data set using Roboflow. They demonstrate how to sign up for an account and create a new project, as well as how to upload and process videos from YouTube to extract frames for annotation. The presenter notes that the frames are not yet annotated and proceeds to upload another video for annotation.
  
  
• 00:05:00 In this section, the video creator demonstrates the process of uploading and annotating images for a football player and ball detection and tracking project. The creator uploads images and extracts frames from a video, assigning the job of annotating images to themselves. They annotate each image with labels for either football or football player and demonstrate annotating different players in each image. Finally, they note that they have annotated a total of 1827 images for the project.
  
  
• 00:10:00 In this section of the video, the presenter talks about preparing the dataset for a football player and ball detection model by resizing all the images to 640x640, filtering out non-annotated images, and generating augmented data to increase the dataset size. The augmented data includes two images with different contrasts and brightness levels generated from a single image. The presenter exports the dataset from Roboflow to a Google Colab file and trains the model using a football dataset extracted from a GitHub repository. They also connect their Google Drive to the Colab notebook to save the weights of the trained model.
  
  
• 00:15:00 In this section, the user sets their current working directory as their GitHub repo and installs all the necessary dependencies to avoid any errors while running the detection or prediction process. They then move towards the required directory detect and download the dataset from roboflow into their Google notebook. The user also downloads the Deep Sort files and unzips them to implement object tracking using Deep Sort. Finally, they train the custom model of YOLOv8 on the football player and football dataset and validate it, ensuring a good mean average. The user can review the training results, including the F1 curve, Precision curve, Recall curve, and training and validation bank results.
  
  
• 00:20:00 In this section, the speaker discusses the results of their YOLOv8 model for football player and ball detection and tracking. They report precise results, with an average precision of 0.63144 and 0.476 for players and good detection of football. The speaker then demonstrates how they downloaded a sample video to test their model and shows the results, as well as placing the model into a live webcam test. Overall, the model worked well with detecting players and assigning unique IDs, but there were some missed detections of football.
  
  
• 00:25:00 In this section, the video shows how to remove the unique ID assigned to the football in the code to simplify the tracking process. The code modifications are made by editing the project.py file and removing the unique ID assigned to the football label. The output video is then downloaded and tested on a live webcam, where the model is successfully able to detect the football but not the player, as they are not dressed like a player. Overall, the modifications made to the code improved the tracking process and produced satisfactory results.
  
  
• 00:30:00 In this section, the presenter demonstrates a script that was written to perform predictions on live webcam using YOLOv8. The script imports YOLO, sets the weights file and performs predictions with a source set to 0 and show set to true. The confidence value is set to 0.15. While dressed as a player, the model was unable to detect the presenter, but the results for detecting a football were successful.

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YOLOv8 and VGG16 for Face, Gender Detection, Face Counting, and People Tracking | Custom Dataset

YOLOv8 and VGG16 for Face, Gender Detection, Face Counting, and People Tracking | Custom Dataset

The video tutorial explains the process of face detection, gender classification, face counting, and people tracking using YOLOv8 and VGG16 models. The tutorial covers various aspects of implementing and training these models, including data preparation, data augmentation, fine-tuning the pre-trained VGG16 model, using transfer learning, and training the YOLOv8 model for face detection. The presenter also explains how to mount a Google Drive in a Google Colab notebook, access and convert image datasets, download required libraries, and integrate object tracking using deepsort. The tutorial provides detailed code explanations for drawing bounding boxes around detected objects, integrating the gender classification model, counting the number of faces in a frame, and assigning each detected face a unique ID using deepsort.update.

• 00:00:00 In this section of the video tutorial, the workflow for face detection with gender classification and face counting with tracking using YOLOv8 and VGG16 is explained. The first step is to prepare the dataset with images of men and women faces, to train the VGG16 model for gender detection, followed by training the YOLOv8 model for face detection. With the face detection from YOLOv8, gender classification is done using the trained VGG16 model. Object tracking is then implemented using Deepsort, assigning a unique ID to each detected face or person. The notebook is divided into nine steps, including importing required libraries, mounting Google Drive, loading the dataset, converting images and labels to arrays, applying data augmentation, fine-tuning the VGG16 model on gender classification data, plotting training and validation loss, and testing with a sample image.
  
  
• 00:05:00 In this section, the speaker discusses various libraries and functions that can be used for converting images to numpy arrays, and vice versa. They also explain the use of two dash categorical library and the concept of sequential and functional approaches to arranging layers in the neural network. The flattened layer is used for converting multi-dimensional inputs into one dimensional, while the dense layer is used for defining the output layer size. Additionally, they discuss the use of transfer learning with the VGG16 model and the import of train test split, numpy, and OS libraries. Finally, they mention the use of the gdob library for accessing all files in a folder and the random library for shuffling image datasets.
  
  
• 00:10:00 In this section, the video explains how to mount a Google Drive with a Google Colab notebook for accessing a dataset, which is uploaded in a zip format. The dataset includes images of men and women faces, and the video shows how to unzip and access the folder containing these images. Using the glob library, the video accesses all the image files in the dataset folders and converts them into an array format with labels indicating whether the image is a man or woman face. The video shows a sample image and explains how the image files variable contains all the image file paths of the men and women folder, which can be read using cb2.im read.
  
  
• 00:15:00 In this section, the speaker explains how they prepared their dataset for face and gender detection. They created a 'men' and 'women' folder, resized the images within them, and converted them into arrays which were then stored in a data list. They appended the corresponding label values into a label list, with 1 for women and 0 for men. The data and label lists were then converted into arrays using NumPy. The speaker also demonstrates data augmentation with the use of an image data generator, generating multiple images from a single image by applying various transformations. They then fine-tuned a pre-trained VGG16 model on their gender classification dataset and implemented softmax activation to define the output layer. The output size was set to 2 to classify either men or women.
  
  
• 00:20:00 In this section of the video tutorial, the speaker demonstrates the VGG16 model for face and gender detection, and shows how to train it on a general classification dataset. The model is saved in the .H5 format, and its accuracy, validation accuracy, and loss parts are calculated. Using cv2.dsize, the image is resized to 100x100 dimensions and converted to an array, and the model predicts whether the image contains a man or a woman. In the next part of the tutorial, the YOLOv8 model will be trained on a face dataset to detect faces and assign a unique ID for tracking. The speaker also mentions that face counting will be implemented using a simple code added to the predict.pi file. Overall, the tutorial is divided into seven steps.
  
  
• 00:25:00 In this section, the presenter introduces the GitHub repository they will use to implement object tracking using deep sort, as well as the YOLO V8 model for face reduction. They discuss how they will detect faces and perform gender classification before integrating deep sort object tracking code to assign each person a unique ID. The presenter then sets their current directory as the cloning repository and installs all required libraries and dependencies needed for the script. They also download the dataset from RoboFlow into their Google Colab notebook, but encounter issues due to having a private account.
  
  
• 00:30:00 be using the weights of the pre-trained YOLOv8 model for face detection. The dataset is downloaded and saved into my Google Drive account, and I have already trained a YOLOv8 model for 80 epochs for face detection. The trained model has already been saved, and the weights have been downloaded into the Google Colab notebook. The deep sort files are also downloaded into the notebook, as object tracking will be implemented using deep sort. Additionally, a VGG16 model has been trained for gender detection, and the model's .h5 file has been saved and downloaded to the Google Drive account. The sample videos are downloaded from the Google Drive to test the predict.pi script, which includes the gender classifier code and face counting.
  
  
• 00:35:00 In this section, the speaker explains the code that was added to implement object tracking using deep sort. The deep sort object tracking port is initialized, and a function is defined to convert the output received from the YOLOv8 model into a compatible format for deep sort. The UI box function creates bounding boxes around the detected objects, while the draw box function calls the UI box and draw border functions to draw a rounded rectangle for the text. The deep sort tracking code is integrated along with the ability to draw trails. The speaker then explains the code for the gender classifier and count function in the Paint file. The count function is used to count each object in each frame. Overall, the code explains how to convert X1, Y1, X2, and Y2 output values from YOLOv8 into center coordinates, height, and width values for object tracking using deep sort, implementing object detection and object tracking in a real-world application.
  
  
• 00:40:00 In this section, the speaker discusses the conversion of the YOLOv8 model into the xcyc center coordinates of the bounding box and width and height of the bounding box to make it compatible with deep sort object tracking. They also explain how the compute color for label function assigns unique colors to the detected objects and the draw Dash border function creates a rectangle above the bounding box where the label and confidence score are written. The speaker also talks about the gender classifier class and how it is loaded and used on each frame of the video to classify the gender of the detected objects. Furthermore, they mention the UI Dash box function and the draw Dash boxes function, which are used to create bounding boxes and call the gender classifier function.
  
  
• 00:45:00 In this section, the presenter explains how to use the gender classification model to detect whether a face belongs to a man or woman. After detecting the face, only the bounding box coordinates are passed to the gender classification model. The model then predicts whether the face belongs to a man or woman, and the label is added above the bounding box accordingly. The presenter then explains the function count, which uses a dictionary called Foundry-classes to store the number of faces detected in the frame. The count is displayed in the UI above the top of the video or image.
  
  
• 00:50:00 In this section, the speaker explains that the count of how many faces were detected in the current frame is stored in the Foundry class dictionary in the FaceDetails class. The dictionary contains two values, the key variable which contains "face" and the value variable which contains how many faces were detected in the current frame. The speaker uses the count function to show how many faces were detected in each frame and assigns a unique ID to each detected face using deepsort.update. The speaker also creates a general class, a classification class, and a danger class. The model detections are tested on multiple demo videos and the speaker shows the results of the detections in each frame.

[MetaQuotes]

Traffic Lights Detection and Color Recognition using YOLOv8 | Custom Object Detection Tutorial

Traffic Lights Detection and Color Recognition using YOLOv8 | Custom Object Detection Tutorial

The video tutorial "Traffic Lights Detection and Color Recognition using YOLOv8" explains the steps to create a traffic light detection and color recognition model using the Ultralytics YOLOv8 web pro. It covers the traffic light dataset, data augmentation, installing necessary libraries, fine-tuning the YOLOv8 model, and testing the model on several videos. The presenter emphasizes the importance of installing all the required libraries, and the results of testing the model on videos demonstrate its accuracy in detecting and recognizing traffic lights of various colors.

• 00:00:00 section, the tutorial covers the traffic light dataset they will use for the project, which consists of around 1000 images with three different classes of traffic lights: green, red, and yellow. They show examples of each label and explain how they applied data augmentation to increase the size of their dataset, since they did not have enough images in their training dataset. The video goes on to show how to export the dataset from RoboFlow into Google Colab notebook and also introduces the newly launched product, Expense, which can help train, deploy, and monitor models, among other features.
  
  
• 00:05:00 In this section, the YouTuber explains the initial steps in the implementation process for creating a traffic light detection and color recognition model using the ultralytics YOLOv8 web pro. The first step involves importing all the necessary libraries, such as OS and glob, which are used for navigating different file paths and plotting input and output images. Next, they check for the presence of a GPU and install all the required libraries using pip. Finally, they clone the ultralytics GitHub repo and set it as the current directory before installing any remaining necessary libraries. The video emphasizes the importance of installing all the required libraries to avoid script errors later on.
  
  
• 00:10:00 In this section of the video, the presenter demonstrates the steps to train and fine-tune a YOLO V8 model on a traffic light dataset using Google Colab. After setting the dataset folder as the current directory, the model is trained on Ade box for 80 boxes, and the results show that the mean average precision with IOU50s for all classes is 98.3%. The confusion matrix is then presented, showing that the model classified green, red, and yellow lights accurately for 96.7%, 97.4%, and 95.5% of the time respectively. The presenter also notes that the loss is continuously decreasing, and the model could be further improved by training it on a higher number of epochs. Finally, the best weights of the model are validated using the validation images.
  
  
• 00:15:00 In this section, the speaker discusses the results of testing the YOLOv8 model on several videos, including a demo video of traffic lights. The model is able to accurately detect traffic lights and assign labels based on the color of the light, with a bounding box color that matches the light's color. The speaker shows examples of the model detecting red, green, and yellow traffic lights, with the appropriate label and bounding box color for each. The results of the model on different videos demonstrate its accuracy in detecting and recognizing traffic lights of various colors.

[MetaQuotes]

Customer Churn Analysis and Prediction using ANN| Deep Learning Tutorial(Tensorflow, Keras & Python)

Customer Churn Analysis and Prediction using ANN| Deep Learning Tutorial(Tensorflow, Keras & Python)

The YouTube video titled "Customer Churn Analysis and Prediction using ANN| Deep Learning Tutorial(Tensorflow, Keras & Python)" demonstrates the use of artificial neural networks to predict customer churn using a dataset from Kaggle. The video covers various steps involved in preparing the data, such as data cleaning, encoding categorical features, and scaling the values in columns. The speaker then creates a neural network with a single hidden layer of 20 neurons and a sigmoid activation function while defining input and output layers and an optimizer with a binary cross-entropy loss function. The accuracy achieved and the classification report using the Scikit-learn library are displayed, with the predicted values being converted into either 0 or 1 form to show an accuracy of 0.78.

• 00:00:00 In this section, the YouTuber introduces the topic of customer churn and explains how artificial neural networks can be used to predict it. They will be using a dataset from Kaggle and Jupyter Notebook to perform exploratory data analysis and data cleaning, followed by data wrangling, train test and split, and eventually predicting customer churn using an artificial neural network. They start by importing the necessary libraries such as Pandas and NumPy and then proceed to load and analyze the dataset, dropping the customer ID column since it is not useful in predicting customer churn.
  
  
• 00:05:00 In this section of the video, the speaker discusses the process of checking the data types and identifying any categorical features. The speaker discovers that the "total charges" column is appearing as a categorical variable, even though it should be a float or integer value. To fix this issue, the speaker converts the column into an integer number. They also encounter an empty space error at position 488 and solve it using errors = "coerce" to ignore the error. Finally, the speaker checks for none values in the rows and plans to drop them as needed.
  
  
• 00:10:00 In this section, the speaker discusses the process of dropping empty rows from the total charges columns using Python. Initially, the speaker identifies the number of empty rows in the total charges column and then applies pd.notnull() to remove them. After dropping the rows, the speaker checks the number of rows left to ensure it matches the expected number. Later, the speaker converts the total charges column to a numerical data type using pd.to_numeric(). The speaker identifies the error of not saving the data type conversion and adjusts it.
  
  
• 00:15:00 In this section of the video, the presenter conducts exploratory data analysis on customer churn data. They convert the "total charges" column to the float type and check the values using visualizations. Then they plot a histogram to see how many loyal customers are staying (not churning) based on a 10-month duration, and color-code the data points which indicate leaving customers in green and those staying in red.
  
  
• 00:20:00 In this section, the speaker discusses modifying statements to find the unique values in each column and adding column names before the unique values. The speaker also talks about finding all columns with categorical variables and converting them into integers or floats. They then define a function to put all of these steps together and be able to print out the categorical values in any dataframe.
  
  
• 00:25:00 In this section of the video, the speaker demonstrates how to use a function to print out categorical values or object data type values for any data frame that is put into it. They then modify their data frame by converting a column to a float data type and removing it. The speaker replaces "no internet service" and "no phone service" with "no" using the replace function and defines a yes-no column where they replace all the yes and no variables with 1 and 0, respectively, to convert categorical values into numerical values, which are easier for machine learning models to understand.
  
  
• 00:30:00 In this section, the speaker discusses the steps involved in preparing the data for machine learning. They demonstrate how to split the dataset into training and testing sets, and how to encode categorical features by replacing "female" with 1 and "male" with 0. The speaker then uses TensorFlow and Keras to create a neural network with a single hidden layer of 20 neurons and a sigmoid activation function. The input layer has 27 features, and the output layer is defined.
  
  
• 00:35:00 In this section of the video, the presenter discusses how to convert text data into integer values using get dummies in order to prepare the data for machine learning models. The presenter shows how to generate dummy values for variables such as internet service and contract, and then uses min max scalar to scale the values in the columns between 0 and 1. The purpose of scaling is to bring the values in the columns between 0 and 1 so that the machine learning model can understand and interpret the data accurately.
  
  
• 00:40:00 In this section, the speaker discusses how to define the input layer, output layer, and optimizer for customer churn analysis and prediction using an artificial neural network. He removes the unnecessary input layer and defines the output layer comprised of one or zero with a sigmoid activation function. He notes that the ReLU function can be used in the hidden layer for classification problems, but not in the output layer. The optimizer is defined with a binary cross-entropy loss function and accuracy is checked with a 100 epoch model compilation. Finally, he displays the accuracy achieved and the classification report using the Scikit-learn library.
  
  
• 00:45:00 In this section, the speaker explains how they converted the predicted values, which were in a 2-dimensional array and ranged from 0 to 1, into either 0 or 1 form. They did this using a for loop, stating that if a value was greater than 0.5, it would be considered as 1, and if it was less than 0.5, it would be considered as 0. After converting the values, the speaker printed the classification report, which shows an accuracy of 0.78. The tutorial concludes with the speaker thanking the viewers for watching and inviting them to subscribe to the channel.