Update README.md

README.md

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-# Kidney Tumor, Cyst, or Stone Classification
-![alt text](https://github.com/Shrey-patel-07/Kidney-Disease-Classifcation/blob/b19262be45c45d9e375e2119d89462ccfc7475c1/templates/kidney_ctscan.png)
-
-## Project Overview
-The main goal of this project is to develop a reliable and efficient deep-learning model that can accurately classify kidney tumors and Stone from medical images.
-
-## Introduction
-Kidney Disease Classification is a project utilizing deep learning techniques to classify Kidney Tumor and Stone diseases from [medical images dataset](https://www.kaggle.com/datasets/nazmul0087/ct-kidney-dataset-normal-cyst-tumor-and-stone/). This project leverages the power of Deep Learning, Machine Learning Operations (MLOps) practices, Data Version Control (DVC). It integrates with DagsHub for collaboration and versioning.
-
-## Dagshub Project Pipeline
-![alt text](https://github.com/Shrey-patel-07/Kidney-Disease-Classifcation/blob/2ad0c02af659c2c1e82798524897d831349b1071/templates/dagshub-kidney_disease_classification.png)
-
-## Mlflow Stats
-![alt text](https://github.com/Shrey-patel-07/Kidney-Disease-Classifcation/blob/2ad0c02af659c2c1e82798524897d831349b1071/templates/mlflow-kidney_disease_classification.png)
-
-## Importance of the Project
-- **Enhancing Healthcare**: By providing accurate and quick disease classification, this project aims to improve patient care and diagnostic accuracy significantly.
-- **Research and Development**: It serves as a tool for researchers to analyze medical images more effectively, paving the way for discoveries in the medical field.
-- **Educational Value**: This project can be a learning platform for students and professionals interested in deep learning and medical image analysis.
-
-## Technical Overview
-- **Deep Learning Frameworks**: Utilizes popular frameworks like TensorFlow or PyTorch for building and training the classification models.
-- **Data Version Control (DVC)**: Manages and versions large datasets and machine learning models, ensuring reproducibility and streamlined data pipelines.
-- **Git Integration**: For source code management and version control, making the project easily maintainable and scalable.
-- **MLOps Practices**: Incorporates best practices in machine learning operations to automate workflows, from data preparation to model deployment.
-- **DagsHub Integration**: Facilitates collaboration, data and model versioning, experiment tracking, and more in a user-friendly platform.
-
-## How to run?
-### STEPS:
-
-Clone the repository
-
-```bash
-https://github.com/krishnaik06/Kidney-Disease-Classification-Deep-Learning-Project
-```
-### STEP 01- Create a conda environment after opening the repository
-
-```bash
-conda create -n venv python=3.11 -y
-```
-
-```bash
-conda activate venv
-```
-
-
-### STEP 02- install the requirements
-```bash
-pip install -r requirements.txt
-```
-
-```bash
-# Finally run the following command
-python app.py
-```
-
-Now,
-```bash
-open up your local host and port
-```
-
-## To Run the Pipeline
-```bash
-dvc repro
-```
 ---
-
-This project is still in development, and we welcome contributions of all kinds: from model development and data processing to documentation and bug fixes.
-
-**Join me in this exciting journey to revolutionize the field of medical image classification with AI!**
+title: Smoething
+sdk: docker
+emoji: 📈
+colorFrom: yellow
+colorTo: blue
+pinned: true
+---