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https://github.com/shaheennabi/production-ready-object_detection-project
An object detection system powered by the YOLOv5s pretrained model 🚀, trained on 2,086 images (1,461 training, 417 validation, 208 testing). Data annotation was done using Roboflow 🖌️, and training took place on Google Colab for 250 epochs.🔥💥 The model achieved high accuracy on both validation and test datasets! 🎯🔥💥
https://github.com/shaheennabi/production-ready-object_detection-project
docker docker-compose github-actions google-colab jenkins object-detection yolov5
Last synced: about 1 month ago
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An object detection system powered by the YOLOv5s pretrained model 🚀, trained on 2,086 images (1,461 training, 417 validation, 208 testing). Data annotation was done using Roboflow 🖌️, and training took place on Google Colab for 250 epochs.🔥💥 The model achieved high accuracy on both validation and test datasets! 🎯🔥💥
- Host: GitHub
- URL: https://github.com/shaheennabi/production-ready-object_detection-project
- Owner: shaheennabi
- License: mit
- Created: 2024-10-08T17:23:00.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-01-02T10:00:21.000Z (about 1 month ago)
- Last Synced: 2025-01-02T11:18:00.884Z (about 1 month ago)
- Topics: docker, docker-compose, github-actions, google-colab, jenkins, object-detection, yolov5
- Language: Jupyter Notebook
- Homepage:
- Size: 63.5 MB
- Stars: 3
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 🎆🎉 Object_detection-End-to-End-project 🎉🎆
### feel free to create a pull request and improve this project...
## Project Overview
* **Objective**: Develop an object detection system using the YOLOv5s pretrained model.
* **Dataset**:
- Total Images: 2,086 images
- Training Images: 1,461
- Validation Images: 417
- Testing Images: 208
- Annotation Tool: Data was annotated using Roboflow for efficient labeling.
* **Model Training**:
- Framework: Utilized the YOLOv5s model, known for its speed and accuracy.
- Environment: Training conducted on Google Colab with GPU support.
- Epochs: The model was trained for 250 epochs, resulting in significant improvements in detection accuracy.
- Performance: The trained model demonstrated great accuracy on the validation and testing datasets.
* **Additional Information**: Further details, including setup instructions and usage, can be found in the project repository.
### 🎋 Project Tree Structure 🎋
```bash
Object_Detection-End-to-End-project/
├── .github/
│ └── workflows/
│ └── main.yaml
├── .jenkins/
│ └── Jenkinsfile
├── artifact/
│ └── 10_05_2024_03_23_14 (or time Stamp)/
│ ├── data_ingestion/
│ │ ├── feature_store/
│ │ │ ├── test
│ │ │ ├── train
│ │ │ ├── valid
│ │ │ └── data.yaml
│ └── SignLangData.Zip
│ ├── data_validation/
│ │ └── status.txt
│ └── model_trainer/
│ └── best.pt
├── data/
│ ├── .gitkeep
│ └── inputImage.jpg
├── flowchart/
├── logs/
├── notebook/
│ ├── Object_Detection_with_Yolov5.ipynb
│ └── test.ipynb
├── scripts/
│ ├── ec2_setup.sh
│ └── jenkins.sh
├── SignLanguage or src/
│ ├── components/
│ │ ├── __pycache__/
│ │ ├── __init__.py
│ │ ├── data_ingestion.py
│ │ ├── data_validation.py
│ │ ├── model_pusher.py
│ │ └── model_trainer.py
│ ├── configuration/
│ │ ├── __pycache__/
│ │ ├── logs/
│ │ ├── __init__.py
│ │ ├── gdown_connection.py
│ │ └── s3_operations.py
│ ├── constants/
│ │ ├── __pycache__/
│ │ ├── __init__.py
│ │ └── training_pipeline/
│ └── entity/
│ ├── __pycache__/
│ ├── __init__.py
│ ├── artifact_entity.py
│ └── config_entity.py
├── templates/
│ └── index.html
├── yolov5/
├── app.py
├── data_collector.py
├── docker_compose.yml
├── Dockerfile
├── README.md
├── requirements.txt
├── setup.py
└── template.py
```
## Installation Instructions
1. Clone the repository:
```bash
git clone https://github.com/shaheennabi/Object_detection-End-to-End-project.git
cd Object_detection-End-to-End-project
```
* Create a new conda environment and activate it:
```bash
conda create -n signLanguage python=3.10 -y
```
```bash
conda activate signlanguage
```
* Install the required dependencies:
```bash
pip install -r requirements.txt
```
### Export the environment variable(anaconda prompt)
```bash
set DATA_DOWNLOAD_URL=https://drive.google.com/uc?/export=download&id= your file_id goes here"
```
### Export the environment variable(git bash)
```bash
export DATA_DOWNLOAD_URL="https://drive.google.com/file/d/ your file_id goes here"
```
# 🎉 Workflow 🎉
After creating the project template:
* 🎇 Update constants 🎆
* 🎇 Update Entity modules 🎆
* 🎇 Update respective component 🎆
* 🎇 Update pipeline 🎆
* 🎉 Endpoint - (app.py) 🎉
## 🎆 Model Training 🎆
1. Ensure the dataset is properly loaded in the `data/` folder.
2. Run the training script:
```bash
python cd yolov5/ && python train.py --img 416 --batch 16 --epochs 300 --data '../data.yaml' --cfg ./models/custom_yolov5s.yaml --weights 'yolov5s.pt' --name yolov5s_results --cache
```
To use the trained model for inference on new images, run:
```bash
python cd yolov5/ && python detect.py --weights /content/runs/train/yolov5s_results/weights/best.pt --img 416 --conf 0.5 --source /content/test/images
```
## Deployment
To deploy the application locally:
1. Install the dependencies in `requirements.txt`.
2. Run the Flask app:
```bash
python app.py
### Deployment (CI/CD)
# Run these commands on EC2 Machine
* Update package lists
```bash
sudo apt update
```
* Update package lists (alternate command)
```bash
sudo apt-get update
```
* Upgrade the system
```bash
sudo apt upgrade -y
```
* Download Docker installation script
```bash
curl -fsSL https://get.docker.com -o get-docker.sh
```
* Run the Docker installation script
```bash
sudo sh get-docker.sh
```
* Add current user to the Docker group (run both lines)
```bash
sudo usermod -aG docker $USER
sudo usermod -aG docker $USER
```
* Install AWS CLI
```bash
sudo apt install awscli -y (if not worked- install with curl -check doc)
```
* AWS Configuration
```bash
aws configure
```
### Run these commands to install Jenkins on EC2
* Update package lists
```bash
sudo apt update
```
* Install OpenJDK 8
```bash
sudo apt install openjdk-8-jdk -y
```
```bash
# Jenkins repository (links for reference)
# https://pkg.jenkins.io/
# https://pkg.jenkins.io/debian-stable/
```
* Start Jenkins service
```bash
sudo systemctl start jenkins
```
* Enable Jenkins to start on boot
```bash
sudo systemctl enable jenkins
```
* Check the status of Jenkins service
```bash
sudo systemctl status jenkins
```
* Download Docker installation script
```bash
curl -fsSL https://get.docker.com -o get-docker.sh
```
* Run the Docker installation script
```bash
sudo sh get-docker.sh
```
* Add current user to Docker group
```bash
sudo usermod -aG docker $USER
```
* Add Jenkins user to Docker group
```bash
sudo usermod -aG docker jenkins
```
* Activate Docker group changes
```bash
newgrp docker
```
* Install AWS CLI
```bash
sudo apt install awscli -y
```
* Add Jenkins to Docker group again to ensure it has access
```bash
sudo usermod -a -G docker jenkins
```
* Download Docker installation script
```bash
curl -fsSL https://get.docker.com -o get-docker.sh
```
* Run the Docker installation script
```bash
sudo sh get-docker.sh
```
* Add current user to Docker group
```bash
sudo usermod -aG docker $USER
```
* Add Jenkins user to Docker group
```bash
sudo usermod -aG docker jenkins
```
* Activate Docker group changes
```bash
newgrp docker
```
* Install AWS CLI
```bash
sudo apt install awscli -y
```
* Add Jenkins to Docker group again to ensure it has access
```bash
sudo usermod -a -G docker jenkins
```
* Retrieve the initial Jenkins admin passwo
```bash
sudo cat /var/lib/jenkins/secrets/initialAdminPassword
```