Gemstones

Deep CNN with residual blocks to classify Gemstones!

Gemstone classification using deep CNNs with residual blocks

Table of Contents

1. About The Project
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. References
5. Contact

About The Project

Classification of gemstones using deep convolutional neural networks with residual blocks.

Built With

• Anaconda 4.10.1
• Python 3.9
• TensorFlow 2.5

Getting Started

Run the following simple steps.

Prerequisites

Anaconda. If you haven't installed Anaconda yet, you can follow the next tutorial: Anaconda Installation.

Installation

1. Clone the repo

   git clone https://github.com/loremendez/Gemstones.git

2. Install the environment
   You can do it either by loading the YML file

   conda env create -f conda_environment.yml

   or step by step
   1. Create and activate the environment

      conda create -n gemstones_env python=3.9
      conda activate gemstones_env

   2. Install the required packages

      pip install --upgrade pip
      pip list  # show packages installed within the virtual environment
      
      pip install tensorflow==2.5
      pip install numpy pandas matplotlib seaborn
      pip install jupyterlab

Usage

Activate the environment, open Jupyter-lab and the notebook Gemstones.ipynb

jupyter-lab

References

[1] Dataset by Chemkaeva, Daria (@LSIND) “Gemstones”. Last updated: 2020-04-27. Link: https://www.kaggle.com/lsind18/gemstones-images

Contact

Lorena Mendez - LinkedIn - lorena.mendez@tum.de

Take a look into my other projects!