https://github.com/rahul-404/end-to-end-chicken-disease-classification
🐔 Chicken Disease Classifier: A web app to classify chicken diseases from fecal images using VGG16 🖼️. Built an end-to-end pipeline with data preprocessing, model fine-tuning (92% accuracy) 📊, and deployed on AWS EC2/ECR for real-time image upload & classification ⚡.
https://github.com/rahul-404/end-to-end-chicken-disease-classification
aws-ec2 aws-ecr classification computer-vision deep-learning flask-application python torch vgg16
Last synced: about 2 months ago
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🐔 Chicken Disease Classifier: A web app to classify chicken diseases from fecal images using VGG16 🖼️. Built an end-to-end pipeline with data preprocessing, model fine-tuning (92% accuracy) 📊, and deployed on AWS EC2/ECR for real-time image upload & classification ⚡.
- Host: GitHub
- URL: https://github.com/rahul-404/end-to-end-chicken-disease-classification
- Owner: Rahul-404
- Created: 2024-12-02T11:31:41.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-12-05T09:03:35.000Z (6 months ago)
- Last Synced: 2025-02-01T22:13:54.351Z (4 months ago)
- Topics: aws-ec2, aws-ecr, classification, computer-vision, deep-learning, flask-application, python, torch, vgg16
- Language: Jupyter Notebook
- Homepage:
- Size: 86.9 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Chicken Disease Classification
1. Intorduction & Github Repository Setup
2. Project Template creation
3. Project Setup & Requirements Installation
4. Logging, Utils & Exception Module
5. Project Workflows
6. All Components Notebook Experiment
7. All Components Module Code Implementation
8. Training Pipeline
9. DVC (MLOps Tool) - For Pipeline Tracking & Implementation
10. Prediction Pipeline & User Application
11. Docker
12. Final CI/CD Deployment on AWS and Azure
## Workflow
1. Update config.yaml
2. Update secrets.yaml [optional]
3. Update params.yaml
4. Update the entity
5. Update the configuration manager in src config
6. Update the components
7. update the pipeline
8. update the main.py
9. update the dvc.yaml