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CycleGAN Image Translation Unpaired Translation Cycle Consistency Open Source

CycleGAN for unpaired image-to-image translation using cycle-consistent adversarial networks. The architecture uses two generators and two discriminators with cycle consistency loss to learn mappings between two image domains without paired training examples. Perfect for learning CycleGAN fundamentals and image translation applications. Complete implementation with PyTorch, including adversarial training, cycle consistency loss, style transfer capabilities, TensorBoard integration, and comprehensive training tools.

CycleGAN PyTorch Image Translation Unpaired Training Download Now Jupyter Notebook TensorBoard Get Started
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CycleGAN Image Translation Project - RSK World
CycleGAN Image Translation Project - RSK World
Image Translation CycleGAN Python PyTorch GAN Computer Vision

This project implements CycleGAN for unpaired image-to-image translation using cycle-consistent adversarial networks. The architecture uses two generators and two discriminators with cycle consistency loss to learn mappings between two image domains without paired training examples. Perfect for learning CycleGAN fundamentals and image translation applications, featuring PyTorch implementation, adversarial training, cycle consistency loss, style transfer capabilities, TensorBoard integration, and comprehensive training tools.

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CycleGAN Architecture

Complete CycleGAN architecture with dual generator-discriminator setup. Uses two generators (G_A2B and G_B2A) and two discriminators (D_A and D_B) to learn bidirectional mappings between two image domains without paired training data.

  • Dual generator-discriminator setup
  • Generator G_A2B and G_B2A
  • Discriminator D_A and D_B
  • Cycle consistency loss

Unpaired Image Translation

Translates images between two domains without paired training examples. Learns mappings from domain A to domain B and vice versa using cycle consistency to ensure meaningful translations.

  • Unpaired training data
  • Bidirectional translation
  • Cycle consistency enforcement
  • No paired examples required

Cycle Consistency Loss

Ensures meaningful translations by enforcing cycle consistency. Images translated from A to B and back to A should match the original, maintaining content while changing style.

  • Forward cycle consistency (A→B→A)
  • Backward cycle consistency (B→A→B)
  • Identity mapping loss
  • Content preservation

Adversarial Training

Complete adversarial training pipeline with dual generators and discriminators. Uses adversarial loss for realistic translations, cycle consistency loss for meaningful mappings, and identity loss for domain preservation.

  • Adversarial loss (GAN loss)
  • Cycle consistency loss
  • Identity mapping loss
  • Training stability improvements

TensorBoard Integration

Real-time training visualization with TensorBoard integration. Monitor generator loss, discriminator loss, gradient penalty, view generated image samples at different resolutions, and track training progress.

  • Real-time loss visualization
  • Generator and discriminator loss tracking
  • Generated image samples at multiple resolutions
  • Interactive dashboard

Jupyter Notebook

Interactive Jupyter Notebook for model architecture visualization, training setup examples, image generation, and latent space exploration.

  • Model architecture visualization
  • Training setup examples
  • Image generation examples
  • Latent space visualization

Style Transfer Applications

Apply style transfer between different image domains. Supports various applications including photo to painting, day to night, summer to winter, and object transfiguration.

  • Photo to painting translation
  • Season transfer (summer/winter)
  • Day to night conversion
  • Object transfiguration

Data Augmentation

Advanced data augmentation strategies including adaptive augmentation, mixup, cutout, and standard augmentation for improved training stability.

  • Adaptive augmentation
  • Mixup augmentation
  • Cutout augmentation
  • Multiple augmentation levels

Web Interface

Interactive Flask-based web application for image translation. Real-time image translation between domains with customizable parameters and download functionality.

  • Flask-based web app
  • Interactive image translation
  • Real-time translation
  • Download translated images

Multiple Dataset Support

Support for multiple datasets including custom unpaired datasets, horse2zebra, apple2orange, and other image translation datasets. Easy dataset switching and custom data loading.

  • Custom unpaired dataset support
  • Horse2Zebra dataset
  • Apple2Orange dataset
  • Season transfer datasets

Resume Training

Resume training from checkpoints with automatic checkpoint detection. Continue training from the last saved epoch without losing progress.

  • Checkpoint resuming
  • Automatic checkpoint detection
  • Training continuation
  • Progress preservation

Advanced Training

Complete training pipeline with learning rate scheduling, gradient clipping, early stopping, label smoothing, and comprehensive training progress logging.

  • Learning rate scheduling
  • Gradient clipping
  • Early stopping
  • Label smoothing
  • Training progress logging

Training Visualization

Comprehensive training visualization with loss curves (adversarial, cycle consistency, identity), translated image samples, domain comparison, and real-time TensorBoard monitoring.

  • Generator/discriminator loss curves
  • Cycle consistency loss tracking
  • Translated image samples
  • Real-time monitoring

Utility Functions

Helper functions for image processing, model utilities, checkpoint management, evaluation metrics, and common development tasks.

  • Image processing utilities
  • Model utility functions
  • Checkpoint management
  • Evaluation helpers

Requirements

The following are the technical requirements for this project:

  • Python 3.8+
  • PyTorch 2.0+
  • NumPy 1.21+
  • Matplotlib 3.5+
  • Jupyter Notebook 1.0.0+
  • TensorBoard 2.10+
  • Pillow 9.0+
  • OpenCV 4.6+

Credits & Acknowledgments

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

  • Python - PSF License
  • PyTorch - BSD License
  • RSK World - Project Inspiration
  • GitHub Repository - Source code and documentation
  • Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks - Original CycleGAN Paper

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
  • CycleGAN Image Translation Documentation
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Categories

Image Translation CycleGAN Python PyTorch GAN Computer Vision

Technologies

Python 3.8+
PyTorch 2.0+
CycleGAN
Cycle Consistency
Image Translation

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