RSK World - Complete Documentation - EfficientNet Image Classification | EfficientNet Image Classification | Deep Learning | Compound Scaling | PyTorch TensorFlow | RSK World

Project Description

This project implements EfficientNet architecture with compound scaling for efficient and accurate image classification, balancing depth, width, and resolution. The architecture achieves better accuracy with fewer parameters compared to ResNet and other models. Includes EfficientNet-B0 through B7 variants for different accuracy-efficiency trade-offs.

EfficientNet uses compound scaling to uniformly scale network depth, width, and resolution. This approach achieves better accuracy with fewer parameters compared to traditional scaling methods. The implementation provides complete PyTorch and TensorFlow/Keras support with comprehensive training, evaluation, and deployment tools.

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Core Features

EfficientNet Architecture

Compound scaling methodology
MBConv blocks with Squeeze-and-Excitation
Mobile Inverted Residual Bottleneck
Better accuracy with fewer parameters
Balanced depth, width, and resolution

Dual Framework Support

Complete PyTorch implementation
Full TensorFlow/Keras implementation
Transfer learning support
Configurable training parameters
Model variant support (B0-B7)

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

Model Export

ONNX export support
TorchScript export
TensorFlow export support
Production-ready formats

API & Deployment

Flask REST API server
Single image prediction endpoint
Batch prediction endpoint
Docker containerization
Docker Compose setup

Advanced Features

Data Management

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

Jupyter Notebooks

EfficientNet Demo notebook
Transfer learning examples
Model comparison analysis
Data augmentation 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

Model Variants

Model	Width	Depth	Resolution	Parameters (M)
EfficientNet-B0	1.0	1.0	224	~5.3
EfficientNet-B1	1.0	1.1	240	~7.8
EfficientNet-B2	1.1	1.2	260	~9.2
EfficientNet-B3	1.2	1.4	300	~12
EfficientNet-B4	1.4	1.8	380	~19
EfficientNet-B5	1.6	2.2	456	~30
EfficientNet-B6	1.8	2.6	528	~43
EfficientNet-B7	2.0	3.1	600	~66

Technologies Used

Python 3.8+ PyTorch 2.0+ TensorFlow 2.13+ Keras 2.13+ EfficientNet Compound Scaling MobileNet Jupyter Notebook Flask Docker

Installation & Usage

Installation

                    # Install basic requirements

                    pip install -r requirements.txt

                    # For API server

                    pip install -r requirements_api.txt

Quick Start

                    from efficientnet_pytorch import efficientnet_b0

                    # Create model

                    model = efficientnet_b0(num_classes=1000)

                    # Use for inference

                    model.eval()

                    output = model(input_tensor)

Training

                    python train.py --train-dir data/train --val-dir data/val --model b0 --epochs 100
                

Inference

                    python inference.py --image path/to/image.jpg --model-path best_model.pth --model b0 --num-classes 1000
                

Evaluation

                    python evaluate.py --test-dir data/test --model-path best_model.pth --model b0 --num-classes 1000
                

API Server

                    python api_server.py --model-path best_model.pth --model b0 --num-classes 1000 --port 5000
                

Docker Deployment

                    # Build image

                    docker build -t efficientnet-classification .

                    # Run container

                    docker-compose up

Project Structure

                efficientnet-classification/

                ├── README.md                          # Main documentation

                ├── requirements.txt                   # Python dependencies

                ├── requirements_api.txt              # API server dependencies

                ├── setup.py                          # Package setup

                ├── LICENSE                           # MIT License

                ├── CONTRIBUTING.md                   # Contribution guidelines

                ├── PROJECT_SUMMARY.md               # Project overview

                ├── Dockerfile                       # Docker configuration

                ├── docker-compose.yml               # Docker Compose config

                │

                ├── Core Implementation

                │   ├── efficientnet_pytorch.py      # PyTorch implementation

                │   ├── efficientnet_tensorflow.py   # TensorFlow/Keras implementation

                │   ├── data_loader.py               # Data loading utilities

                │   └── utils.py                     # Helper functions

                │

                ├── Training & Inference

                │   ├── train.py                     # Training script

                │   ├── inference.py                 # Inference script

                │   ├── evaluate.py                  # Evaluation script

                │   ├── benchmark.py                 # Benchmarking script

                │   └── test_model.py                # Model testing

                │

                ├── Data & Preprocessing

                │   ├── data_preparation.py          # Data preparation utilities

                │   └── data/                        # Data directories

                │       ├── raw/                     # Raw images

                │       ├── processed/               # Processed images

                │       └── augmented/               # Augmented images

                │

                ├── Visualization & Analysis

                │   └── visualize.py                 # Visualization tools

                │

                ├── Deployment

                │   ├── api_server.py                # Flask API server

                │   ├── export_model.py              # Model export utilities

                │   └── scripts/                     # Helper scripts

                │

                ├── Notebooks

                │   ├── EfficientNet_Demo.ipynb     # Main demo

                │   ├── Transfer_Learning.ipynb     # Transfer learning

                │   ├── Model_Comparison.ipynb       # Model comparison

                │   └── Data_Augmentation.ipynb      # Data augmentation

                │

                └── Examples

                    ├── example_usage.py             # Usage examples

                    └── demo.html                    # Demo web page

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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EfficientNet Image Classification