YOLO v4 and TF 2.0

🎓 Master Object Detection with YOLOv4 & TensorFlow 2.0: A Comprehensive Course for Android Developers

Course Instructor: Nandakishor Mcourse

Course Title: Custom Object Detection Training using YOLOv4 and TensorFlow 2.0 with Google Colab and Android Deployment

Hi everyone! 👋

Welcome back to the world of cutting-edge computer vision technology! In this course, you'll embark on a journey to master two of the most advanced State Of The Art (SOTA) object detection architectures: YOLOv4 and TensorFlow 2.0. I've designed this course to be both comprehensive and practical, ensuring that you not only understand the theoretical underpinnings but also can apply your knowledge in real-world scenarios, including Android app deployment.

Course Structure:

Module Breakdown:

1. Anaconda Installation - Setting up your development environment.

2. Image Dataset Resizing - Preparing your datasets for optimal performance.

3. Image Dataset Labeling - A one-time labeling strategy to streamline your workflow.

4.🔍 YOLO to PASCAL VOC Conversion - Adapting YOLOv4 for TensorFlow 2.0 compatibility.

5. YOLOv4 Training and tflite Conversion on Google Colab - Leveraging cloud computing for your training needs.

6.📱 YOLOv4 Android Deployment - Integrating YOLOv4 into an Android app.

7. SSD Mobilenet TF2.0 Training and tflite Conversion on Google Colab - Exploring alternative object detection methods.

8.📱 SSD Mobilenet Android Deployment - Deploying SSD Mobilenet on an Android platform.

9. YOLOv4 and SSD Technical Details - A deep dive into the technical aspects of both architectures.

Technical Deep Dive:

Understanding Object Detection:

• Precision & Recall - Measuring your model's performance.
• IoU (Intersection Over Union) - Evaluating the overlap between predicted and actual bounding boxes.
• mAP/AP (Mean Average Precision/Average Precision) - The standard metric for object detection.
• Batch Normalization - Ensuring robustness during training.
• Residual Blocks - Building deep neural networks.
• Activation Function - Choosing the right function to introduce non-linearity.
• Max Pooling - Reducing dimensionality and computational cost.
• Feature Pyramid Networks (FPN) - Creating multi-scale feature representations.
• Path Aggregation Network (PAN) - Merging path features in a memory efficient way.
• SPP (spatial pyramid pooling layer) - Pooling across spatial dimensions.
• Channel Attention Module (CAM) and Spatial Attention Module (SAM) - Focusing the network on important regions of the input image.

YOLOv4 - Technical Details:

• Backbone - The base neural network architecture.
• Cross-Stage-Partial-connections (CSP) - Connecting different stages of the network.
• YOLO with SPP - Combining YOLO with a spatial pyramid pooling layer for better feature extraction.
• PAN in YOLOv4 - Path Aggregation Network to enhance multi-scale context aggregation.
• Spatial Attention Module (SAM) in YOLOv4 - Enhancing the model's ability to focus on relevant areas.
• Bag of freebies (Bof) and Bag of specials (BoS) - Advanced features that give YOLOv4 an edge.

SSD - Technical Details:

• Architecture Overview and Working - Understanding the SSD framework.
• Loss Functions - Tailoring the training process with appropriate loss measures.

YOLO vs SSD:

• Speed and Accuracy Benchmarking - Comparing YOLOv4 and SSD on performance metrics.

By the end of this course, you'll not only understand the intricacies of YOLOv4 and TensorFlow 2.0 but also be able to deploy your models directly into Android applications. Get ready to unlock the full potential of object detection in real-time applications with YOLO v4 and TF 2.0: Custom Object Detection Training using Google Colab and Android Deployment. 🚀

Let's dive into the exciting world of computer vision together! Enroll now and transform your skills with state-of-the-art object detection technologies.