YOLO Beverage Container Detection

A real-time multi-class beverage container detection system powered by YOLOv8 with a modern React frontend and FastAPI backend. Detect bottles, wine glasses, and cups in live webcam feeds or uploaded videos.

Overview

This project provides an end-to-end solution for detecting beverage containers in images and video streams. It leverages the YOLOv8 object detection model, optimized for identifying common beverage-holding objects such as plastic bottles, wine glasses, and cups.

Use Cases:

Recycling automation systems
Inventory management for bars/restaurants
Smart retail shelf monitoring
Environmental monitoring for litter detection
Educational demonstrations of computer vision

Features

Real-time WebSocket Detection - Stream webcam frames for instant object detection
Video Upload Processing - Upload videos and receive frame-by-frame detection results via Server-Sent Events (SSE)
Adjustable Confidence Threshold - Dynamically tune detection sensitivity via UI slider
Multi-class Detection - Identify bottles, wine glasses, and cups simultaneously
Rolling Benchmark Statistics - Track inference latency with avg, p95, and p99 metrics
Responsive React UI - Modern interface with live bounding box overlays
Docker Ready - One-command deployment with Docker Compose
CORS Enabled - Easy integration with external frontends

Architecture

+------------------+       WebSocket (frames)      +------------------+
|                  | ----------------------------> |                  |
|   React Frontend |                               |  FastAPI Backend |
|   (Vite + Nginx) | <---------------------------- |   (YOLOv8 + UV)  |
|                  |       JSON (detections)       |                  |
+------------------+                               +------------------+
        |                                                   |
        |  HTTP POST /upload                                |
        | ---------------------------------------------->   |
        |                                                   |
        |  SSE (progress + results)                         |
        | <----------------------------------------------   |
        |                                                   |
        v                                                   v
+------------------+                               +------------------+
|   User Webcam    |                               |  YOLOv8n Model   |
|   or Video File  |                               |   (6.5 MB .pt)   |
+------------------+                               +------------------+

Component Overview

Component	Technology	Purpose
Frontend	React 19 + Vite 8	User interface, webcam capture, visualization
Backend	FastAPI + Uvicorn	API server, WebSocket handler, video processing
Model	YOLOv8n (Nano)	Object detection inference
Proxy	Nginx	Static file serving, WebSocket proxy
Container	Docker Compose	Orchestration and deployment

Detected Classes

The model detects the following beverage container classes from the COCO dataset:

Class ID	Label	Description
39	bottle	Plastic bottles, water bottles, soda bottles
40	wine glass	Wine glasses, champagne flutes
41	cup	Coffee cups, mugs, plastic cups

The model uses YOLOv8n pre-trained on COCO and filters detections to only these beverage-related classes.

Quick Start

Docker Deployment (Recommended)

The fastest way to get started is using Docker Compose:

# Clone the repository
git clone <repository-url>
cd yolo-demo

# Start all services
docker compose up --build

# Access the application
# Frontend: http://localhost:3000
# Backend API: http://localhost:8000
# API Docs: http://localhost:8000/docs

Stop the services:

docker compose down

View logs:

docker compose logs -f backend
docker compose logs -f frontend

Local Development

Prerequisites

Python 3.11+
Node.js 20+
pip or uv package manager

Backend Setup

cd backend

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run the server
python main.py

# Server will start at http://localhost:8000

Frontend Setup

cd frontend

# Install dependencies
npm install

# Start development server
npm run dev

# Frontend will start at http://localhost:5173

API Documentation

WebSocket Endpoint

`WS /ws/detect`

Real-time object detection via WebSocket connection.

Message Types (Client -> Server):

Binary Frame - JPEG-encoded image bytes
```
Send raw JPEG bytes for detection
```

Config Message - JSON text to update settings

{
  "type": "config",
  "threshold": 0.5
}

Response Types (Server -> Client):

Detection Result

{
  "type": "detection",
  "detections": [
    {
      "class_id": 39,
      "label": "bottle",
      "confidence": 0.923,
      "bbox": [120.5, 80.2, 250.8, 400.1]
    }
  ],
  "inference_ms": 23.45,
  "avg_ms": 25.12,
  "p95_ms": 32.50,
  "p99_ms": 45.20,
  "frames": 30
}

Config Acknowledgment

{
  "type": "config_ack",
  "confidence": 0.5
}

Error

{
  "type": "error",
  "detail": "could not decode frame"
}

REST Endpoint

`POST /upload`

Upload a video file for batch processing with SSE progress streaming.

Request:

Content-Type: multipart/form-data
Body: file - Video file (MP4, WebM, MOV)

Response: Server-Sent Events stream

SSE Event Types:

Start Event

{
  "type": "start",
  "job_id": "uuid",
  "total_frames": 1500,
  "fps": 30
}

Progress Event (per frame)

{
  "type": "progress",
  "frame": 100,
  "total_frames": 1500,
  "progress": 6.7,
  "detections": [...]
}

Complete Event

{
  "type": "complete",
  "job_id": "uuid",
  "total_frames": 1500,
  "results": [...]
}

Benchmarks

Performance benchmarks measured on different hardware configurations:

Inference Latency (YOLOv8n)

Hardware	Avg (ms)	P95 (ms)	P99 (ms)	FPS
Apple M1 (CPU)	45	52	58	~22
Apple M2 Pro (CPU)	32	38	42	~31
Intel i7-12700K (CPU)	38	45	50	~26
NVIDIA RTX 3060 (GPU)	8	10	12	~125
NVIDIA RTX 4090 (GPU)	3	4	5	~333

Note: Benchmarks include model inference only, not pre/post-processing or network latency.

Model Comparison

Model	Size	mAP@50	Inference (CPU)	Inference (GPU)
YOLOv8n	6.5 MB	37.3	45 ms	8 ms
YOLOv8s	22 MB	44.9	85 ms	12 ms
YOLOv8m	52 MB	50.2	180 ms	18 ms
YOLOv8l	84 MB	52.9	320 ms	25 ms
YOLOv8x	131 MB	53.9	480 ms	35 ms

Memory Usage

Component	RAM Usage
Backend (idle)	~500 MB
Backend (active)	~1.2 GB
Frontend (Nginx)	~15 MB
Model (YOLOv8n)	~50 MB

Throughput (Video Processing)

Resolution	FPS (CPU)	FPS (GPU)
480p	18-22	80-100
720p	12-15	60-80
1080p	6-8	40-50

Model Information

YOLOv8 Nano (yolov8n.pt)

Architecture: YOLOv8 (Ultralytics)
Variant: Nano (smallest, fastest)
Size: 6.5 MB (3.2M parameters)
Training Data: COCO 2017 (80 classes)
Input Size: 640x640 (auto-scaled)
Output: Bounding boxes + class probabilities

Using a Custom Model

To use your own trained YOLOv8 model:

Place your .pt file in the models/ directory

Update the MODEL_PATH environment variable:

export MODEL_PATH=/app/models/your-model.pt

Or modify docker-compose.yml:

environment:
  - MODEL_PATH=/app/models/your-model.pt

Training Your Own Model

# Install ultralytics
pip install ultralytics

# Train on custom dataset
yolo detect train data=your-dataset.yaml model=yolov8n.pt epochs=100

# Export to ONNX for faster inference (optional)
yolo export model=runs/detect/train/weights/best.pt format=onnx

Training with Google Colab

Train a custom beverage detection model using Google Colab's free GPU and Roboflow for dataset management.

Custom Dataset Classes

The custom Roboflow dataset includes 9 beverage container classes:

Class ID	Label	Description
0	bottle-glass	Glass bottles
1	bottle-plastic	Plastic bottles
2	cup-disposable	Disposable cups
3	cup-handle	Cups with handles
4	glass-mug	Glass mugs
5	glass-normal	Regular glasses
6	glass-wine	Wine glasses
7	gym bottle	Sports/gym bottles
8	tin can	Tin cans

Colab Training Notebook

Create a new Colab notebook and run the following cells:

Cell 1: Install Dependencies

!pip install ultralytics roboflow

Cell 2: Download Dataset from Roboflow

from roboflow import Roboflow

rf = Roboflow(api_key="YOUR_ROBOFLOW_API_KEY")
project = rf.workspace("").project("")
dataset = project.version(1).download("yolov8")

Get your API key from: Roboflow Dashboard → Settings → API Key

Cell 3: Train the Model

from ultralytics import YOLO

# Load pretrained YOLOv8n
model = YOLO('yolov8n.pt')

# Train on custom dataset
results = model.train(
    data='/content/beverage-containers-1/data.yaml',
    epochs=100,
    imgsz=640,
    batch=16,
    name='beverage-detector'
)

Cell 4: Validate the Model

# Run validation
metrics = model.val()
print(f"mAP50: {metrics.box.map50:.3f}")
print(f"mAP50-95: {metrics.box.map:.3f}")

Cell 5: Download Trained Model

from google.colab import files

# Download best weights
files.download('/content/runs/detect/beverage-detector/weights/best.pt')

Using Your Trained Model

Download best.pt from Colab

Place it in the models/ directory:

mv ~/Downloads/best.pt models/beverage-model.pt

Update docker-compose.yml:

environment:
  - MODEL_PATH=/app/models/beverage-model.pt

Update class filtering in backend/detector.py to use class IDs 0-8 instead of COCO IDs

Alternative: Upload Dataset Manually

If you prefer not to use the Roboflow API:

Zip your dataset locally:

zip -r dataset.zip train/ valid/ test/ data.yaml

Upload and unzip in Colab:

from google.colab import files

# Upload the zip file
uploaded = files.upload()

# Unzip
!unzip dataset.zip

Train with local data.yaml:

model.train(data='/content/data.yaml', epochs=100)

Configuration

Environment Variables

Variable	Default	Description
`MODEL_PATH`	`../models/yolov8n.pt`	Path to YOLOv8 model weights
`VITE_API_URL`	(auto-detect)	Backend API URL for frontend
`VITE_WS_URL`	(auto-detect)	WebSocket URL for frontend

Detection Parameters

Parameter	Default	Range	Description
`confidence`	0.45	0.1-0.9	Minimum confidence threshold
`window_size`	30	10-100	Rolling window for benchmark stats

Project Structure

yolo-demo/
├── backend/
│   ├── main.py              # FastAPI application entry point
│   ├── detector.py          # YOLOv8 wrapper class
│   ├── benchmark.py         # Latency tracking utilities
│   ├── requirements.txt     # Python dependencies
│   ├── Dockerfile           # Backend container definition
│   └── .dockerignore        # Docker build exclusions
│
├── frontend/
│   ├── src/
│   │   ├── App.jsx          # Main React component
│   │   ├── App.css          # Application styles
│   │   ├── components/
│   │   │   ├── Webcam.jsx   # Webcam capture component
│   │   │   ├── Canvas.jsx   # Bounding box overlay
│   │   │   └── VideoUpload.jsx # Video upload UI
│   │   └── hooks/
│   │       ├── useDetection.js # WebSocket hook
│   │       └── useUpload.js    # Upload/SSE hook
│   ├── package.json         # Node.js dependencies
│   ├── vite.config.js       # Vite configuration
│   ├── nginx.conf           # Production nginx config
│   ├── Dockerfile           # Frontend container definition
│   └── .dockerignore        # Docker build exclusions
│
├── models/
│   └── yolov8n.pt           # Pre-trained YOLOv8 nano weights
│
├── docker-compose.yml       # Multi-service orchestration
└── README.md                # This file

Troubleshooting

Common Issues

WebSocket Connection Failed

Ensure backend is running on port 8000
Check firewall settings
Verify CORS configuration

Model Not Found

Confirm yolov8n.pt exists in models/ directory
Check MODEL_PATH environment variable

Slow Inference

Consider using GPU acceleration
Reduce input resolution
Use a smaller model variant (yolov8n)

Docker Build Fails

Ensure Docker Desktop is running
Check disk space (needs ~5GB)
Try docker compose build --no-cache

Contributing

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit changes (git commit -m 'Add amazing feature')
Push to branch (git push origin feature/amazing-feature)
Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

Ultralytics YOLOv8 - State-of-the-art object detection
FastAPI - Modern Python web framework
React - UI component library
COCO Dataset - Training data for pre-trained models
Roboflow - Dataset management and annotation
Google Colab - Free GPU for model training

Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
backend		backend
frontend		frontend
.gitignore		.gitignore
README.md		README.md
docker-compose.yml		docker-compose.yml

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YOLO Beverage Container Detection

Table of Contents

Overview

Features

Architecture

Component Overview

Detected Classes

Quick Start

Docker Deployment (Recommended)

Local Development

Prerequisites

Backend Setup

Frontend Setup

API Documentation

WebSocket Endpoint

WS /ws/detect

REST Endpoint

POST /upload

Benchmarks

Inference Latency (YOLOv8n)

Model Comparison

Memory Usage

Throughput (Video Processing)

Model Information

YOLOv8 Nano (yolov8n.pt)

Using a Custom Model

Training Your Own Model

Training with Google Colab

Custom Dataset Classes

Colab Training Notebook

Using Your Trained Model

Alternative: Upload Dataset Manually

Configuration

Environment Variables

Detection Parameters

Project Structure

Troubleshooting

Common Issues

Contributing

License

Acknowledgments

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