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MobileNet V2 Image Classification Deep Learning Open Source

MobileNet V2 architecture optimized for mobile and edge devices with inverted residual blocks and linear bottlenecks for efficient image classification. The architecture uses depthwise separable convolutions to reduce computational cost while maintaining accuracy. Complete implementation with PyTorch and TensorFlow, including width multiplier variants (α=0.35 to 1.4), transfer learning, and comprehensive evaluation tools.

MobileNet V2 PyTorch TensorFlow Mobile & Edge Download Now Jupyter Notebook Inverted Residuals Get Started
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MobileNet V2 Image Classification Project - RSK World
MobileNet V2 Image Classification Project - RSK World
Deep Learning Image Classification Python PyTorch TensorFlow Computer Vision

This project implements MobileNet V2, a lightweight CNN architecture designed for mobile and embedded vision applications. The architecture uses inverted residual blocks with linear bottlenecks and depthwise separable convolutions to reduce computational cost while maintaining accuracy. Perfect for real-time image classification on resource-constrained devices, featuring PyTorch and TensorFlow implementations, width multiplier variants (α=0.35 to 1.4), transfer learning, TFLite conversion, and comprehensive visualization tools.

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MobileNet V2 Architecture

MobileNet V2 architecture with inverted residual blocks and linear bottlenecks designed for mobile and edge devices. Uses depthwise separable convolutions for efficient computation.

  • Inverted residual blocks with linear bottlenecks
  • Depthwise separable convolutions
  • Width multiplier (α) from 0.35 to 1.4
  • Optimized for mobile and edge devices

PyTorch & TensorFlow Implementation

Complete implementations in both PyTorch and TensorFlow/Keras frameworks for flexibility and comparison.

  • PyTorch implementation with advanced features
  • TensorFlow/Keras implementation
  • Transfer learning support
  • Configurable training parameters

Model Visualization & Analysis

Comprehensive visualization tools for model architecture, predictions, training curves, and performance metrics.

  • Architecture visualization
  • Prediction visualization
  • Training history plots
  • Model comparison charts

Mobile Deployment & Real-time Inference

Convert models to TensorFlow Lite format for mobile deployment and perform real-time inference on single images or webcam feeds.

  • TensorFlow Lite conversion
  • Model quantization support
  • Real-time webcam classification
  • Single image and batch prediction

Performance Metrics & Visualization

Detailed evaluation with confusion matrix, training curves, and comprehensive performance metrics.

  • Confusion matrix visualization
  • Training history plots
  • Accuracy and precision metrics
  • Model performance comparison

Jupyter Notebook

Interactive Jupyter Notebooks for MobileNet V2 training, evaluation, transfer learning, and model comparison.

  • MobileNet V2 demo notebook
  • Transfer learning examples
  • Model comparison analysis
  • Data augmentation techniques

Advanced Data Augmentation

Enhance training data with advanced augmentation techniques including MixUp, CutMix, Random Erasing, and AutoAugment.

  • MixUp augmentation
  • CutMix augmentation
  • Random Erasing
  • AutoAugment support

Model Analysis Tools

Comprehensive model analysis including parameter counting, model size analysis, and comparison utilities.

  • Parameter counting
  • Model size analysis
  • Model comparison tools
  • Performance benchmarking

Configuration Management

YAML-based configuration system for easy customization of training parameters and model settings.

  • YAML configuration files
  • Easy parameter customization
  • Training configuration
  • Model settings management

Sample Data Utilities

Tools to prepare and organize training data with sample data generation and structure creation.

  • Sample data generation
  • Data structure creation
  • Dataset organization tools
  • Training data preparation

Data Preprocessing

Robust data preprocessing pipeline for image dataset preparation, normalization, and augmentation.

  • Image dataset loading
  • Data normalization
  • Train/val/test split
  • Image preprocessing utilities

Width Multiplier Variants

Support for different width multipliers (α) from 0.35 to 1.4 with different accuracy-efficiency trade-offs for various use cases.

  • α=0.35 (~1.7M parameters, ~7 MB)
  • α=0.5, 0.75, 1.0, 1.3, 1.4 variants
  • Configurable width multiplier
  • Optimized for different device constraints

Training Visualization

Comprehensive visualization utilities for training history, metrics, and prediction analysis.

  • Training history plots
  • Confusion matrix visualization
  • Prediction visualization
  • Model comparison charts

Utility Functions

Helper functions for logging, file management, model utilities, and common development tasks.

  • Logging setup and management
  • Directory creation utilities
  • Model utility functions
  • Common development helpers

Requirements

The following are the technical requirements for this project:

  • Python 3.7+
  • TensorFlow 2.10+
  • PyTorch 1.12+ (optional)
  • Keras 2.10+
  • NumPy, Pillow, Matplotlib, OpenCV
  • Jupyter Notebook 1.0.0+

Credits & Acknowledgments

This project is developed for educational purposes and utilizes the following resources:

  • Python - PSF License
  • PyTorch - BSD License
  • TensorFlow - Apache 2.0 License
  • Keras - Apache 2.0 License
  • RSK World - Project Inspiration
  • GitHub Repository - Source code and documentation

Support & Contact

For paid applications, please contact us for integration help or feedback.

  • Support Email: help@rskworld.in
  • Contact Number: +91 9330539277
  • Website: RSKWORLD.in
  • GitHub Project
  • Join Our Discord
  • Slack Support Channel
  • MobileNet V2 Image Classification Documentation
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Categories

Deep Learning Image Classification Python PyTorch TensorFlow Computer Vision

Technologies

Python 3.8+
Keras
PyTorch
TensorFlow
Mobile & Edge

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About RSK World

Founded by Molla Samser, with Designer & Tester Rima Khatun, RSK World is your one-stop destination for free programming resources, source code, and development tools.

Founder: Molla Samser
Designer & Tester: Rima Khatun

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