Welcome to the Product Tagging with Convolutional Neural Networks (CNN) project! 🚀 Imagine having an AI-powered system that automatically tags your product images for e-commerce, saving you tons of time and effort. That’s exactly what this project is all about.
This project builds an AI model using Convolutional Neural Networks (CNN) to automate the process of tagging product images. Whether you're an e-commerce enthusiast or just curious about deep learning, this project is a great hands-on example of how you can train models to classify images and predict product categories.
- 🔥 Powerful CNN Architecture: Leveraging the capabilities of CNN for image classification.
- 📊 90.13% Accuracy: Achieved after training on just 2 epochs!
- 🔄 Fully Automated: From loading the data to making predictions.
Excited to try it out? Here's how you can dive right into it:
- Clone the repository:
git clone https://github.com/KunalParkhade/product-tagging-cnn.git
- Navigate into the project directory:
cd product-tagging-cnn - Install the required dependencies:
pip install -r requirements.txt
Once you're set up, you can open the Jupyter Notebook (Product_Tagging.ipynb) and start running the cells!
This project is structured to guide you through the entire process of building an AI model for product tagging:
- Dataset Preparation: We start by loading and preprocessing product images.
- Building the CNN: The core of this project is a Convolutional Neural Network designed to recognize patterns in images.
- Training the Model: Watch the magic happen as the model learns to classify product images.
- Testing & Results: Finally, evaluate the model’s performance. Spoiler alert: it achieves a solid 90.13% accuracy! 🏆
Follow the steps in the notebook to:
- Load your dataset
- Train the CNN on your data
- Evaluate the model’s predictions
You'll see just how effective CNNs are at recognizing patterns and classifying images. Plus, it's easy to tweak the model parameters (like batch size, number of epochs) if you're feeling adventurous.
After just 2 epochs of training, our CNN model reaches an impressive 90.13% accuracy on the test set. And that’s only the beginning! There are plenty of ways to enhance this project, from experimenting with more advanced architectures to fine-tuning hyperparameters.
- Hands-on learning: If you're new to deep learning or image classification, this is a perfect project to get your hands dirty!
- Real-world application: Product tagging is widely used in e-commerce and retail—this project could be a stepping stone for similar solutions.
- Engaging: Who doesn’t want to see a model they built correctly classify images?
Go ahead, ⭐ star this repo and start exploring! Your contribution and feedback are always welcome.
We’ve built the foundation, but there’s so much more you can do:
- 🧠 Try more advanced models like ResNet or EfficientNet.
- 🎛️ Experiment with different hyperparameters or data augmentation techniques.
- 🏷️ Use this model to build an actual product-tagging app for an e-commerce platform.
Feel free to fork this repository, raise issues, or submit pull requests. Let’s make this project even better together!
This project is licensed under the MIT License, so feel free to use it as you wish!
Made by Kunal Parkhade