Master Computer Vision & Deep Learning: OpenCV, YOLO, ResNet

Gain an overview of artificial intelligence and see how computer vision, machine learning, and deep learning fit within it, plus image basics like pixels, channels, and color models.

Discover how artificial intelligence enables machines to mimic human intelligence through computer vision, machine learning, and deep learning, with neural networks extracting high-level features from data for real-world applications.

Computer vision enables machines to see, identify, and process images like humans by automatically extracting information and labeling what is present through classification, localization, object detection, and segmentation.

Explore image fundamentals by understanding pixels, channels, and color models, including RGB, LAB, and HSV, with grayscale and binary imaging insights and 0-255 value ranges and color perception concepts.

Guide learners through a step-by-step tool setup for Ubuntu and Windows environments, then explore PyCharm, Jupyter Notebook, and Google Colab for training models.

guide students through setting up Ubuntu tools for computer vision development by installing python 3.6, optionally other stable versions, and PyCharm, following on-screen commands and the provided setup manual.

Install Python version 3.6 in the C folder and set up PyCharm on Windows using the provided links. Use the setup manual in resources to complete the Windows tool installation.

Learn how to install and launch PyCharm, create or open projects, configure Python interpreters and virtual environments, install packages, run and debug Python code efficiently.

Create, rename, upload, and open notebooks in Google Colab, then run code in cells with shared variables. Select cpu, gpu, or tpu runtimes and note 12 hours of execution.

Explore the basic building block of deep learning—the neuron—and its architecture, then study artificial neural networks, convolutional neural networks, and the role of activation functions.

Understand how a neuron functions as the computational unit in neural networks, receiving inputs through dendrites and sending outputs via axons, powering deep learning.

Neurons model brain using inputs, weights, and a bias to produce 0 or 1. They form an artificial neural network that classifies inputs via a weighted sum and activation function.

Explore how a convolutional neural network analyzes images with convolutional layers, feature maps, pooling, and fully connected layers for classification, highlighting fewer parameters and fast training.

Activation functions determine a neural network’s output, accuracy, and training efficiency, acting as gates that activate neurons and define binary step, linear, and non-linear activations for complex data.

Explore object detection in computer vision, comparing early models like Haar Cascade and Hog with advanced models such as R-CNN and YOLO, highlighting speed, dataset size, and accuracy.

Compare one-stage and two-stage object detectors by describing single-network detection versus a two-step region proposal process. Retinanet and Yolo illustrate one-stage models; R-CNN, Faster R-CNN and FPN illustrate two-stage detectors.

Differentiate object detection and object tracking by drawing boundary boxes and classifying objects in each frame, then track them across video with unique IDs.

R-CNN uses selective search to generate about 2000 region proposals, extracts features with a convolutional network, classifies with an SVM, and refines bounding boxes with regression, though training is time-consuming.

Learn how Fast R-CNN reduces computation by sharing features across regions of interest, using RoI pooling, softmax classification, and bounding box regression, making it faster and more accurate than R-CNN.

Explore how region proposal networks predict objectness and bounding boxes across a backbone feature map, using anchors and 3x3 and 1x1 convolutions to generate object proposals.

Explore R-FCN, a region-based fully convolutional detector that shares computation across the image, using ResNet-101 feature maps, RPN proposals, and position-sensitive RoI pooling to classify RoIs.

Explore the project object detection with Faster R-CNN by examining the high-level design, performing a code walkthrough, and following download and execution instructions to run the project.

Perform object detection with faster r-cnn to identify 88 object types in a marketplace video, with a PyCharm walkthrough and downloadable code.

Download and unzip Faster-RCNN.zip, open the project in PyCharm, and run faster_rcnn_Object Detection.py with the pre-trained frozen_inference_graph.pb and coco.names for 88 object classes on TownCenterXVID.avi input after installing requirements.txt.

Explore the object detection model and its architecture, starting with the net model from Facebook, then cover the euro be three model, the tiny model, and the iPhone model.

RetinaNet, introduced by Facebook AI Research, targets dense and small object detection with a ResNet-based backbone, a feature pyramid, and two subnetworks for classification and regression using focal loss.

Discover how the SSD model performs real-time object detection by predicting bounding boxes and class scores in a single end-to-end CNN, using multi-scale feature layers and non-maximum suppression.

Explore YOLOv4, an efficient object detector for production and parallel computation, featuring CSPDarknet53 backbone with SPPnet and PANet, three detection heads, and bag of freebies and specials that boost accuracy.

Learn how to perform license plate recognition using yolov3, explore the project design, walk through the source code, and follow execution and download instructions.

Demonstrate license plate recognition from webcam video using YOLO and a convolutional neural network to generate bounding boxes. Walk through PyCharm code and provide download instructions for execution.

Follow a code walkthrough for license plate detection with YOLOv3, OpenCV, and pytesseract, covering project setup in PyCharm, virtual environments, and running the model on videos, webcam, and images.

Download and unzip the License_Number_Plate_Detection_YOLOv3.zip from resources. Open PyCharm to access name.py and model.py, review test_dataset and yolo_utils with configuration, weights, and class name files, then run main.py.

Explore how image classification works in industry by examining the classification pipeline and comparing key models: SVM, decision tree, and K-NN.

Master image classification in computer vision with supervised learning, labeling images by content, and tackle challenges like scale and occlusion while reviewing models such as SVM, VGG16, and ResNet50.

Explore the four-stage image classification pipeline, from pre-processing with grayscale conversion, standardizing image sizes, and data augmentation, to object segmentation, feature extraction and training, and final object classification.

Explore SVM for classification and regression in high-dimensional spaces, using kernel-based projection to maximize the margin and reduce overfitting in machine vision tasks.

Explore the decision tree, a supervised classifier for remote sensing images. Learn how root, interior, and leaf nodes split data using gain ratio and Gini index, and avoid overfitting.

Apply the knn algorithm, a simple, nonparametric, lazy supervised learner for image classification and regression, using euclidean distance and majority voting to label new points.

Explore the YOLOv3 license plate project, review its design and architecture, walk through the code download instructions, and learn configuration, setup, and Google Colab training.

Train a YOLOv3 model for license plate detection using transfer learning with a pretrained convnet as fixed feature extractor, then train a linear classifier.