RealTime Ai Animal Identification With Deep Learning & Cv

Real-Time Ai Animal Identification With Deep Learning & Cv
Published 5/2025
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

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.

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

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