ecommerce-gan-augmentation/
├── 📁 data/
│ ├── 📁 raw/
│ ├── 📁 processed/
│ ├── 📁 augmented/
├── 📁 models/
│ ├── 📁 generator/
│ ├── 📁 discriminator/
│ ├── 📁 trained_model/
├── 📁 notebooks/
│ ├── 📓 data_preprocessing.ipynb
│ ├── 📓 model_training.ipynb
│ ├── 📓 model_evaluation.ipynb
├── 📁 scripts/
│ ├── 📝 train_gan.py
│ ├── 📝 evaluate_gan.py
│ ├── 📝 deploy_model.py
├── 📄 README.md
├── 📄 requirements.txt
└── 🗂️ .gitignore
-
Install the required packages:
pip install -r requirements.txt
-
Preprocess the dataset:
jupyter notebook notebooks/data_preprocessing.ipynb
-
Train the GAN model:
python scripts/train_gan.py
-
Evaluate the GAN model:
jupyter notebook notebooks/model_evaluation.ipynb
-
Deploy the GAN model as a web service:
python scripts/deploy_model.py
- Generate new product images: Access the deployed model at
http://localhost:5000/generate
. - Data preprocessing and model evaluation: Use the provided notebooks.
This project leverages the power of Generative Adversarial Networks (GANs) to augment product images for e-commerce platforms. The GAN generates high-resolution images from different angles and in various settings, enhancing product listings to attract more customers and boost sales.
- 🔍 Data Preprocessing: Clean and prepare raw product images for training.
- 🧠 Model Training: Train a GAN to generate realistic and diverse product images.
- 📊 Model Evaluation: Assess the performance of the trained GAN.
- 🌐 Deployment: Deploy the GAN as a web service to generate images on demand.
✨ Enhance your e-commerce platform with stunning product images! ✨
Feel free to reach out if you have any questions or need further assistance. Happy coding! 💻🚀