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ResNet-50 Image Classification Deep Learning Open Source

Deep Residual Network with 50 layers for high-accuracy image classification using skip connections and residual blocks. Complete implementation with PyTorch and TensorFlow, including Grad-CAM visualization, model ensemble, and advanced data augmentation.

ResNet-50 PyTorch TensorFlow Grad-CAM Download Now Jupyter Notebook Transfer Learning Get Started
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ResNet-50 Image Classification Project - RSK World
ResNet-50 Image Classification Project - RSK World
Deep Learning Image Classification Python PyTorch TensorFlow Computer Vision

This project implements ResNet-50, a deep convolutional neural network with 50 layers that uses residual connections to enable training of very deep networks. The architecture includes identity shortcuts that allow gradients to flow directly through layers, solving the vanishing gradient problem. Perfect for image classification tasks with high accuracy, featuring PyTorch and TensorFlow implementations, Grad-CAM visualization, model ensemble, and advanced data augmentation techniques.

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ResNet-50 Architecture

Deep Residual Network with 50 layers using skip connections and residual blocks to solve vanishing gradient problem.

  • 50-layer deep architecture
  • Residual connections for gradient flow
  • ImageNet pre-trained weights
  • High accuracy image classification

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

Grad-CAM Visualization

Visualize which parts of images are important for model predictions using Gradient-weighted Class Activation Mapping.

  • Grad-CAM visualization tools
  • Model attention visualization
  • Prediction explanation
  • Interactive visualization

Model Ensemble & Batch Prediction

Combine multiple models for improved accuracy and predict on multiple images at once.

  • Model ensemble utilities
  • Batch image prediction
  • Directory scanning support
  • Multiple voting methods

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 Notebook for ResNet-50 training, evaluation, and experimentation.

  • Interactive training notebook
  • Step-by-step analysis
  • Model experimentation
  • Visualization examples

Advanced Data Augmentation

Enhance training data with advanced augmentation techniques including Mixup, Cutout, and Test Time Augmentation.

  • Mixup augmentation
  • Cutout augmentation
  • Test Time Augmentation (TTA)
  • Configurable augmentation pipeline

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

Transfer Learning

Leverage ImageNet pre-trained weights for faster training and improved accuracy on custom datasets.

  • ImageNet pre-trained weights
  • Fine-tuning support
  • Feature extraction mode
  • Custom classifier layers

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 3.8+
  • PyTorch 2.0+
  • TensorFlow 2.13+
  • Keras 2.13+
  • NumPy, PIL, Matplotlib
  • 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
  • ResNet-50 Image Classification Documentation
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Categories

Deep Learning Image Classification Python PyTorch TensorFlow Computer Vision

Technologies

Python 3.8+
Keras
PyTorch
TensorFlow

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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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