Instructions to use AnamikaP/Fake-Product-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use AnamikaP/Fake-Product-Model with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://AnamikaP/Fake-Product-Model") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
π§ Fake Product Analyzer
An AI-powered system that detects product logos from images using YOLOv8 and classifies them as Real or Fake using ResNet50. Built as part of my AI/ML project during the CDAC Training Program.
π§ Status: This project is still in development. More features and improvements coming soon!
π Problem Statement
Counterfeit products cause major financial and health-related issues. This project aims to:
- Detect logos from product images
- Classify the logo as Real or Fake
- Provide explanation or reasoning using an LLM
- Offer an easy-to-use Streamlit interface
π Tech Stack Used
| Component | Technology |
|---|---|
| Object Detection | YOLOv8 |
| Image Classifier | ResNet50 |
| LLM Integration | OpenAI |
| Frontend | Streamlit |
| Language | Python |
πΌ Sample Output
| Input Image | Detected Logo | Classification | Explanation |
|---|---|---|---|
![]() |
Nestle | Fake | The logo font doesn't match official Nestle branding. |
π How to Run Locally
- Clone the repo:
git clone https://github.com/yourusername/Fake-Product-Analyzer.git
- Downloads last month
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