https://github.com/utkarsh251106/smart-inventory
A Computer Vision project using YOLO11n for detecting and counting fruits and vegetables in an image or a video stream. It sends Telegram alerts if the item count drops below 5 for more than 5 seconds.
https://github.com/utkarsh251106/smart-inventory
artificial-intelligence computer-vision deep-learning machine-learning objec opencv python ultralytics yolo11-detection
Last synced: about 2 months ago
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A Computer Vision project using YOLO11n for detecting and counting fruits and vegetables in an image or a video stream. It sends Telegram alerts if the item count drops below 5 for more than 5 seconds.
- Host: GitHub
- URL: https://github.com/utkarsh251106/smart-inventory
- Owner: Utkarsh251106
- Created: 2025-02-04T19:43:01.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-06T13:21:16.000Z (about 1 year ago)
- Last Synced: 2025-10-27T23:39:26.470Z (8 months ago)
- Topics: artificial-intelligence, computer-vision, deep-learning, machine-learning, objec, opencv, python, ultralytics, yolo11-detection
- Language: Jupyter Notebook
- Homepage:
- Size: 463 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Security: docs/SECURITY.md
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README
## 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:
```bash
git clone https://github.com/Utkarsh251106/Smart-Inventory
```
### Step 2: Create a conda environment:
```bash
conda create -n venv python=3.12.7 -y
conda activate venv
```
### Step 3: Install the requirements:
```bash
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
```bash
# 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
```bash
# Start the Jupyter Notebook environment using the command
streamlit run app.py
```