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+title = "🎨 From Photos to Monet: CycleGAN and Styled CycleGAN for Artistic Style Transfer"
+description = "Exploring GAN-based architectures to transform real-world photos into Monet-inspired paintings, with custom enhancements for better style fidelity."
+date = 2025-11-04
+[taxonomies]
+tags = ["machine_learning"]
+[extra]
+styles = ["notebooks.css", ]
++++
+
+## Project Overview
+
+Art and AI intersect in this project, which tackles the Kaggle challenge *“I’m
+Something of a Painter Myself.”* The goal: generate Monet-style images. Instead
+of creating paintings from scratch, I focused on **style
+transfer**—transforming real-world photos into Monet-inspired artworks using
+**CycleGAN**, and later improving the approach with a custom **Styled
+CycleGAN**.
+
+The dataset includes:
+
+* **Monet paintings:** 300 images
+* **Photos:** 7,038 images  
+
+All images are 256×256 RGB, stored as TFRecords.
+
+The challenge lies in preserving content while transferring style—a delicate
+balance between realism and artistry.
+
+## Approach
+
+The workflow began with **CycleGAN**, an architecture designed for unpaired
+image-to-image translation. It uses two generators and two discriminators to
+ensure that style transfer happens without losing the original content. Think
+of it like translating English to French and back to English—the round trip
+should preserve meaning.
+
+Key steps:
+
+* **Data Analysis:** Pixel intensity distributions, structural similarity
+metrics, and visualizations.
+* **CycleGAN Implementation:** Built with TensorFlow and Keras using UNet-based
+generators and PatchGAN discriminators.
+* **Loss Functions:** Adversarial, cycle-consistency, and identity losses.
+* **Styled CycleGAN:** Introduced **style loss** and **content loss** using
+VGG19 for perceptual features, improving artistic fidelity.
+* **Hyperparameter Tuning:** Experimented with dropout rates and epochs to
+balance clarity and style.
+
+## Key Findings
+
+* **Plain CycleGAN struggled** to produce noticeable style changes even after
+50 epochs.
+* **Styled CycleGAN introduced strong Monet-like patterns early (10 epochs)**,
+but required tuning to reduce distortions.
+* Best configuration: **Styled CycleGAN, 50 epochs, dropout=0.5**, achieving a
+MiFID score of **59.90** (better than CycleGAN’s 70.76).
+* Increasing dropout to 0.7 improved clarity but reduced artistic feel;
+lowering it to 0.3 worsened results.
+
+## Reflections
+
+Styled CycleGAN clearly outperformed the baseline, showing that perceptual
+losses (style and content) are critical for artistic tasks. However:
+
+* Balancing style transfer with content preservation remains tricky.
+* GPU limitations restricted deeper experiments—reducing cycle and identity
+loss weights might improve results.
+
+Future improvements could include:
+
+* Exploring alternative architectures (e.g., Diffusion Models).
+* Reducing cycle-consistency weight for more freedom in style transfer.
+* Ignoring original photos entirely, as Kaggle rules allow.
+
+***
+
+If you're curious about the details, the full notebook is embedded below 👇
+
+<!-- markdownlint-disable MD033 -->
+<iframe title="Monet Painting CycleGAN notebook" class="notebook-embed" src="notebook.html"></iframe>
+
+You can also view the notebook in [a separate page](notebook.html), or check it
+on [GitHub](https://github.com/Farzat07/Kaggle-Mini-Project-Monet-Painting-Dataset).