Real-Time Ai Animal Identification With Deep Learning & Cv

Tracking the Wild: Real-Time Animal Identification Using Deep Learning with Python & Computer Vision

What you'll learn

Learn real-time animal detection and ID basics, and their role in wildlife monitoring, conservation, and ecological research.

Set up Python with TensorFlow/Keras for deep learning and OpenCV for image preprocessing to enable robust, real-time animal classification.

Explore the EfficientNetB0 model, optimized for accurate animal classification across 90 species, and its application in real-time video feeds.

Learn data preprocessing techniques, including image normalization, resizing, and augmentation, to improve model performance and generalization.

Implement real-time visualization of identified animals by annotating video frames with bounding boxes, species labels, and confidence scores.

Utilize Flask as a backend framework to serve detection results and integrate with an interactive web-based dashboard for monitoring.

Enable global streaming of real-time detection using Ngrok, allowing remote access to the live video feed from any location.

Implement MQTT for efficient transmission of detected animal data, facilitating seamless communication with IoT-based monitoring systems.

Address challenges such as occlusions, variations in lighting conditions, and species with similar features by optimizing model training and inference strateg

Use the system for wildlife conservation, anti-poaching, and research focused on habitat monitoring and biodiversity tracking.

Requirements

Basic understanding of Python programming (helpful but not mandatory).

A laptop or desktop computer with internet access [Windows OS with Minimum 4GB of RAM).

No prior knowledge of AI or Machine Learning is required—this course is beginner-friendly.

Enthusiasm to learn and build practical projects using AI and IoT tools.

Description

Welcome to the AI-Powered Real-Time Animal Identification System Course! In this hands-on course, you'll learn how to build a real-time animal detection and identification system using the EfficientNetB0 deep learning model. This project leverages Flask for backend development, MQTT for seamless data communication, and Ngrok for global live streaming—allowing remote access to real-time wildlife monitoring.This course focuses on leveraging EfficientNetB0 to classify and identify 90 different animal species from a Kaggle dataset, providing valuable insights for conservationists, researchers, and AI enthusiasts. By the end of this course, you'll have developed a fully functional system capable of detecting animals in real-time, displaying results through a web-based interface.What You Will Learn:● Set up your Python development environment with essential libraries like TensorFlow/Keras, OpenCV, and Flask.● Use EfficientNetB0, a high-performance deep learning model, to classify animals from real-time video feeds.● Preprocess images and video streams by applying normalization, resizing, and augmentation for optimal detection accuracy.● Implement a Flask-based web dashboard to visualize identified animals with bounding boxes, species names, and confidence scores.● Enable global streaming using Ngrok, allowing real-time animal identification from anywhere in the world.● Integrate MQTT for efficient communication between detection models and the web interface.● Overcome challenges such as variations in lighting conditions, occlusions, and species with similar physical features.● Apply your knowledge to wildlife conservation, ecological research, and AI-driven biodiversity tracking.Enroll today and start building your Tracking the Wild: Real-Time Animal Identification Using Deep Learning

Overview

Section 1: Introduction to Animal Detection System

Lecture 1 Course Introduction and Features

Section 2: Environment Setup for Python Development

Lecture 2 Installing Python

Lecture 3 VS Code Setup for Python Development

Section 3: Animal Detection System Project Overview

Lecture 4 Animal Detection System Project Overview

Section 4: Setting Up Google Colab and Mounting Google Drive

Lecture 5 Setting Up Google Colab and Mounting Google Drive

Section 5: Dataset Download and Exploration

Lecture 6 Dataset Download and Exploration

Section 6: Dataset Preprocessing and Augmentation

Lecture 7 Dataset Preprocessing and Augmentation

Section 7: Splitting the Dataset for Training, Validation, and Testing

Lecture 8 Splitting the Dataset for Training, Validation, and Testing

Section 8: Visualizing the Animal Dataset and Augmented Data

Lecture 9 Visualizing the Animal Dataset and Augmented Data

Section 9: EfficientNetB0 Model Implementation

Lecture 10 EfficientNetB0 Model Implementation

Section 10: Training the EfficientNetB0 Model and Monitoring Progress

Lecture 11 Training the EfficientNetB0 Model and Monitoring Progress

Section 11: Model Inference using Flask and Ngrok

Lecture 12 Model Inference using Flask and Ngrok

Section 12: Code Execution

Lecture 13 Code Execution

Section 13: Wrapping Up

Lecture 14 Course Wrap-Up

Students looking to dive into AI and gain hands-on experience in real-time animal detection using EfficientNetB0, Flask, and MQTT.,Working professionals wanting to upskill in AI, deep learning, and computer vision for applications in wildlife monitoring and conservation.,IoT enthusiasts who want to integrate AI-driven animal identification into Internet of Things (IoT) solutions for smart surveillance and ecological research.,Aspiring developers aiming to build a career in AI, machine learning, or computer vision, with a focus on real-time detection and streaming technologies.,Wildlife researchers and conservationists interested in leveraging AI to track and monitor species in real-world environments.