Brand Image Recognition with CLIP
Overview
This project aims to classify product images based on their brand using the CLIP model. The CLIP (Contrastive Language-Image Pretraining) model, developed by OpenAI, enables learning visual concepts from textual descriptions.
Project Structure
Data Preparation: Images and brand labels are used to train and evaluate the model. Data Augmentation: The dataset has been augmented to improve model performance and robustness. Model Training: The CLIP model is used to extract features from images and text, which are then used to train a classification model. Testing: The trained model is evaluated on a test dataset to assess its performance. Files and Directories
brands.csv: CSV file containing the image filenames and their corresponding brand names. brandsImages/: Directory containing images of the products. Setup
Install Dependencies Ensure you have Python and the necessary libraries installed. To create a new environment based on environment.yml file, use the command: conda env create -f environment.yml
Prepare Data First, files are created after the dataset is augmented. To do this, run the file PrepareData.py . Ensure that the augmented_brands.csv file and augmentedBrandsImages directory are in the correct locations specified in the script. First, files are created after the dataset is augmented. To do this, run the file PrepareData.py . And only after that run the main file TrainModel.py