This Repo contains a Box Detection Application capable of identifying box containers in conveyor belt pictures.

• Accurate Counting: Automatically count boxes on conveyor belts for real-time inventory updates, reducing manual counting errors and delays.
• Box Type Identification: Differentiate between different types of boxes based on size, shape, or markings, enabling efficient sorting and tracking.
• Damage Detection: Identify damaged boxes during transportation, allowing for quicker intervention and quality control.

1. Install Docker
   • Make sure Docker is installed on your system. You can download and install Docker Desktop from the official Docker website.
2. Pull the Docker Image
   • Open docker desktop, followed by a terminal or command prompt in your system and run:

     docker pull agpsuai23/box_detection_image

   • This command will download the Docker image named agpsuai23/box_detection_image to your local machine.
3. Run the Docker Container
   • Once the image is pulled, execute the following command:

     docker run -p 8000:8000 agpsuai23/box_detection_image

   • This command starts the container and maps port 8000 of the host to port 8000 of the container, allowing access to the application.
4. Access the Application
   • Open a web browser and go to: http://localhost:8000/
   • You can now interact with the Box Detection Application through the browser interface.

1. Computer Vision based Data Science & ML
   • Yolo8_Model_Creation.ipynb downloads the dataset from Roboflow, followed by training Yolov8 on train set using transfer learning, validation and test result display. We end up with a mean Average Precision (mAP) of 0.913 (90%+). Finally, the model is saved as best.pt
2. Software Engineering using FAST API
   • I created a FastAPI web application (main.py) to serve the saved model to customers.
   • Home page (Upload_for_Detection.html) is a basic I/O based UI that lets a user upload a conveyor belt image, and when a button is clicked, the application uses the previously saved Yolo8 model to detect box containers and return the image with labels in a new page. Code can be found in main.py - @app.post("/YOLO_Box_Prediction_Website/")
   • Alternatively, I also created an API functionality that can programmatically accept a conveyor belt image as input to return as output the image with labels saved in local system. Working can be found at main.py - @app.post("/YOLO_Box_Prediction_Service/"). An example of API call can be found in CUSTOM_YOLO_API_call.ipynb
3. Deployment using Docker Containerization
   • Finally, I deployed this FastAPI based Yolov8 application as a Docker Image (based on Dockerfile) and uploaded it to Docker Hub.
   • All they need to do is to simply install Docker Desktop, pull the image from the hub, run it as a Docker container and finally access the webpage on a browser as localhost:8000
   • Advantage of containerization is that all end users can avoid the hassle of installing packages mentioned in requirements.txt, which are necessary to run the application on their system.