https://github.com/lokk798/rice-leaf-disease-mlops
Rice Leaf Disease Detection using MLOps | This project applies deep learning CNN to detect rice leaf diseases while following MLOps best practices, and leverages DVC for data versioning.
https://github.com/lokk798/rice-leaf-disease-mlops
cnn computer-vision deep-learning dvc machine-learning-pipeline mlops
Last synced: over 1 year ago
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Rice Leaf Disease Detection using MLOps | This project applies deep learning CNN to detect rice leaf diseases while following MLOps best practices, and leverages DVC for data versioning.
- Host: GitHub
- URL: https://github.com/lokk798/rice-leaf-disease-mlops
- Owner: lokk798
- License: mit
- Created: 2024-12-13T13:28:56.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-29T23:32:28.000Z (over 1 year ago)
- Last Synced: 2025-03-30T00:23:59.152Z (over 1 year ago)
- Topics: cnn, computer-vision, deep-learning, dvc, machine-learning-pipeline, mlops
- Language: Jupyter Notebook
- Homepage:
- Size: 2.74 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Rice Leaf Disease Detection - MLOps Pipeline
**Rice Leaf Disease Detection using MLOps** | This project applies deep learning CNN to detect rice leaf diseases while following **MLOps best practices**, and leverages **DVC for data versioning**.
---
## **Workflow Overview**
This project follows an **MLOps pipeline** with clearly defined stages:
1️⃣ **Update `config.yaml`** → Define project configurations
2️⃣ **Update `params.yaml`** → Set hyperparameters and model settings
3️⃣ **Update the Entity** → Create structured entity classes for data handling
4️⃣ **Update the Configuration Manager (`src/config`)** → Manage configurations efficiently
5️⃣ **Update the Components** → Implement modular ML components (data processing, training, evaluation)
6️⃣ **Update the Pipeline** → Integrate all components into a seamless pipeline
7️⃣ **Update `main.py`** → Entry point to trigger the pipeline
8️⃣ **Update `dvc.yaml`** → Define DVC pipeline stages for data and model versioning
---
## **Getting Started**
### **🔹 Setup Environment**
```bash
# Clone the repository
git clone https://github.com/lokk798/rice-leaf-disease-mlops.git
cd rice-leaf-disease-mlops
# Install dependencies
pip install -r requirements.txt
```
# Run the App
```bash
python app.py
```
### **🔹To Set Up DVC**
```bash
# Initialize DVC
dvc init
```
### **🔹 To Run the Pipeline**
```bash
dvc repro
```
## App Screenshots
### Image Upload
### Prediction Result
