real estate image classification 30classes LLM Model

Introduction

The Real-Estate-Image-Classification-30Classes model is an image classification model designed to categorize real estate images into 30 distinct classes. It is created using PyTorch and is part of the Hugging Face ecosystem, leveraging the Transformers library and TensorBoard for visualization. The model achieves an accuracy of approximately 66.67%.

Architecture

This model is built using the ViT (Vision Transformer) architecture, which is well-suited for image classification tasks. ViT processes images by dividing them into patches and applying transformer layers to capture relationships between different parts of an image.

Training

The model was trained using the HuggingPics framework, which automates the creation of image classifiers. The training process involved optimizing for accuracy, with TensorBoard used to track and visualize training metrics.

Guide: Running Locally

To run the Real-Estate-Image-Classification-30Classes model locally, follow these steps:

Set Up Environment: Ensure you have Python installed, along with PyTorch and Transformers libraries. Optionally, TensorBoard can be used for logging.
Clone the Repository: Clone the model repository from Hugging Face to your local machine.
```
git clone https://huggingface.co/andupets/real-estate-image-classification-30classes
```
Install Dependencies: Navigate into the cloned directory and install any required dependencies.
```
cd real-estate-image-classification-30classes
pip install -r requirements.txt
```
Execute the Model: Run the model script to classify images. You can use a Jupyter Notebook or a Python script to load and test the model with sample images.
Cloud GPU Suggestion: For enhanced performance, especially with larger datasets, consider using cloud-based GPUs such as Google Colab, which provides free access to powerful GPUs.

License

The Real-Estate-Image-Classification-30Classes model is available under a specific license provided by the Hugging Face model repository. Ensure to review the license details for compliance and appropriate use.

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