Yolo11m Model

Glow Patch

Overview

In machine learning and computer vision, tackling the problem of detection of road potholes at night has been a critical step in furthering the safety of AI-powered vehicle transportation. In toward of solving this problem we present a baseline object detector using YOLOv11 and fine-tuning on the Nighttime Pothole Dataset (NPD). Over 50 epochs the model the model achieved an [email protected] score of 0.93245, a 1.6% improvement compared to results found in Ling et. al's paper.

We use Fiftyone to streamline the COCO dataset sanitization and cleaning process. Training using the Ultralytics library we deploy our model using Flask, FastAPI, Docker, and AWS.

Use our solution at Glow Patch

This project was developed during the Voxel51 AI Hackathon at the University of British Columbia and we extended the functionality by deploying the app afterwards.

📊 Data Sanitzation

Using the Fiftyone library and GUI we computed image embeddings to seperate images into many categories and filtered to ensure a balanced dataset.

Fiftyone Embeddings

📈 Training

Over the 50 epochs we see a convergent increase in precision score

Training graph results

🌟 Results

Our training resulted in favorable results as seen on the prediction of the validation set:

Validation predictions picture

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Project Workflow

1. Data Sanitization → Filtering out images based on computed embeddings to ensure a balanced dataset.
2. Training & Validation → Fine-tuned pre-trained model on NPD on NVIDIA 4060 Laptop graphics card, validated on small set.
3. Deployment → Dockerized inference service and deployed on AWS ECS with a integrated front-end.

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Tech Stack

Technology	Usage
Fiftyone	Data visualization and filtration
Ultralytics	Fine-tuning and model inference
Python	Main programming language
HuggingFace	Model API creation
Next.js	Front-end

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Usage & Installation

Website

Our service is live and hosted on Glow Patch, check it out!

Installation Prerequisites

You must have Python 3.9 and Docker installed along with the required packages in both /requirements.txt and api/requirements.txt.

1️⃣ Clone the Repository

git clone https://github.com/asalee01/Glow-patch.git

2️⃣ Install Dependencies

pip install -r requirements.txt
cd api
pip install -r requirements.txt

3️⃣ Build Docker Image

docker build -t glow-patch .

4️⃣ Run the Program

docker run -d --name container -p 80:80 glow-patch

Now the app should be running locally on port 80.

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Future Improvements

✅ Video support for real-time object detection on web app.

✅ Real-time object detection with live video input.

✅ Port webapp to iOS, Android with Tesla compatability.

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Contributions

All contributions are welcome, open to pull requests and suggestions.

📩 Contact: [email protected] | 🌐 LinkedIn: Ali Osman

📩 Contact: [email protected] | 🌐 LinkedIn: Athif Saleem

📩 Contact: [email protected] | 🌐 LinkedIn: Kaseya Xia

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License

This project is licensed under the MIT License. See LICENSE for details.