Description

This project utilizes YOLOv11n for detecting and counting vegtables and fruits in an image or a video streams. It processes the video to identify and count the number of items in each frame, alerting the user via Telegram if the tomato count drops below 5 for more than 5 seconds. The project is designed to handle real-time video input and provide continuous monitoring of the detected object, sending notifications when needed.

How to run it?

Step 1: Clone the Repository:

git clone https://github.com/Utkarsh251106/Smart-Inventory

Step 2: Create a conda environment:

conda create -n venv python=3.12.7 -y
conda activate venv

Step 3: Install the requirements:

pip install -r requirements.txt

Step 4: To find the model:

Follow this path to get the model -> model/best.pt

Step 5: To run the code(for Fruit-and-Vegetable-detection files):

To run the code

# Start the Jupyter Notebook environment using the command
jupyter notebook

Run your Code_for_images.ipynb file for detection in an image in the notebooks folder

Run your Code_for_video.ipynb file for detections in a video in the notebooks folder

Step 6(Optional): To run the streamlit file(present in the fruit-veg-detector folder):

To run the code

# Start the Jupyter Notebook environment using the command
streamlit run app.py