RSK World - Complete Documentation - MobileNet V2 Image Classification | MobileNet V2 Image Classification | Deep Learning | Inverted Residual Blocks | PyTorch TensorFlow | RSK World

Project Description

This project implements MobileNet V2 architecture, a lightweight CNN designed for mobile and embedded vision applications. It uses inverted residual blocks with linear bottlenecks and depthwise separable convolutions to reduce computational cost while maintaining accuracy. Includes configurable width multipliers (α=0.35 to 1.4) for different accuracy-efficiency trade-offs.

MobileNet V2 uses inverted residual blocks with linear bottlenecks to achieve high accuracy with low computational cost. The architecture is optimized for real-time inference on resource-constrained devices. The implementation provides complete PyTorch and TensorFlow/Keras support with comprehensive training, evaluation, TFLite conversion, and deployment tools for mobile and edge devices.

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Core Features

MobileNet V2 Architecture

Inverted residual blocks
Linear bottlenecks for efficiency
Depthwise separable convolutions
Optimized for mobile devices
Low computational cost, high accuracy

Dual Framework Support

Complete PyTorch implementation
Full TensorFlow/Keras implementation
Transfer learning support
Configurable training parameters
Width multiplier variants (α=0.35-1.4)

Training & Evaluation

Complete training pipeline
Validation loop with metrics
Model checkpointing
Learning rate scheduling
Training history tracking

Visualization Tools

Architecture visualization
Prediction visualization
Training curves plotting
Model comparison charts
Confusion matrix analysis

Mobile Deployment

TensorFlow Lite conversion
Model quantization support
Real-time webcam classification
Edge device optimization
Production-ready mobile formats

Real-time Inference

Single image classification
Webcam real-time classification
Pre-trained ImageNet support
Batch prediction support
Low latency inference

Advanced Features

Data Management

PyTorch DataLoader support
TensorFlow data generators
Custom dataset class
Data augmentation transforms
Dataset splitting utilities

Jupyter Notebooks

MobileNet V2 Demo notebook
Transfer learning examples
Width multiplier comparison
Mobile optimization techniques

Benchmarking

Performance comparison across models
Inference time measurement
Memory usage tracking
FPS calculation

Utilities

Training/validation loops
Model saving/loading
Parameter counting
History plotting
Confusion matrix generation

Width Multiplier Variants

Width Multiplier (α)	Parameters (M)	Model Size (MB)	Top-1 Accuracy (ImageNet)	Use Case
0.35	~1.7	~7	~60%	Ultra-lightweight devices
0.5	~2.0	~8	~64%	Basic mobile devices
0.75	~2.6	~10	~69%	Mid-range devices
1.0 (Default)	~3.5	~14	~72%	Standard mobile devices
1.3	~5.3	~21	~74%	High-end devices
1.4	~6.1	~24	~75%	Premium devices

Technologies Used

Python 3.7+ PyTorch 1.12+ TensorFlow 2.10+ Keras 2.10+ MobileNet V2 Inverted Residuals TFLite Jupyter Notebook Real-time Inference Edge Devices

Installation & Usage

Installation

# Install basic requirements
pip install -r requirements.txt

# For API server
pip install -r requirements_api.txt

Quick Start

import tensorflow as tf
from models.mobilenet_v2 import get_mobilenet_v2_tf

# Create MobileNet V2 model
model = get_mobilenet_v2_tf(
    input_shape=(224, 224, 3),
    num_classes=1000,
    alpha=1.0  # Width multiplier
)

# Or use pre-trained ImageNet model
model = tf.keras.applications.MobileNetV2(weights='imagenet')

Training

python train.py --data_dir data/train --epochs 50 --batch_size 32 --alpha 1.0

Inference (Single Image)

python inference.py --image path/to/image.jpg

# With custom trained model
python inference.py --model_path models/mobilenet_v2_trained.h5 --image path/to/image.jpg

Real-time Webcam Classification

python inference.py --webcam

TFLite Conversion for Mobile

from model_utils import convert_to_tflite

convert_to_tflite(model, 'models/mobilenet_v2.tflite', quantize=True)

Demo (Pre-trained Model)

# Quick demo without training
python demo.py

Project Structure

                mobilenet-classification/

                ├── README.md                          # Main documentation

                ├── requirements.txt                   # Python dependencies

                ├── config.py                          # Configuration settings

                ├── setup.py                           # Automated setup

                ├── LICENSE                            # MIT License

                ├── PROJECT_SUMMARY.md                 # Project overview

                │

                ├── Core Implementation

                │   ├── models/

                │   │   ├── __init__.py

                │   │   └── mobilenet_v2.py           # MobileNet V2 (TensorFlow & PyTorch)

                │   ├── train.py                       # Training script

                │   ├── inference.py                   # Inference script

                │   ├── data_utils.py                  # Data loading & preprocessing

                │   └── model_utils.py                 # Model save/load/convert utilities

                │

                ├── Setup & Testing

                │   ├── setup.py                       # Automated setup script

                │   ├── quick_start.py                 # Test installation

                │   ├── demo.py                        # Quick demo with pre-trained model

                │   └── create_sample_data.py          # Generate sample dataset

                │

                ├── Advanced Tools

                │   ├── benchmark.py                   # Performance benchmarking

                │   ├── visualize.py                   # Visualization utilities

                │   └── example_usage.py               # Code examples

                │

                └── Documentation

                    └── mobilenet_v2_classification.ipynb  # Jupyter notebook tutorial

Contact Information

Get in Touch

Developer: Molla Samser
Designer & Tester: Rima Khatun

rskworld.in

help@rskworld.in support@rskworld.in

+91 93305 39277

License

This project is for educational purposes only. See LICENSE file for more details.

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MobileNet V2 Image Classification