●14 reads●MIT License
Objectify.ai | Django Object Detection Web App (Model: SSD MobileNet V3)
Table of contents
Django Object Detection Web App
Overview
This is a Django-based web application for object detection using a pre-trained deep learning model. Users can upload an image, and the system will process it to detect objects using OpenCV and TensorFlow.
Coder: Tanvir Anjom Siddique
Features
- Upload images for object detection
- Process images using a pre-trained model (SSD MobileNet v3)
- Can detect 80 types of objects
- Display detected objects with number of occurance and processed images with bounding boxes
- Bootstrap-based UI for a responsive experience
Live Demo: Running YOLO v3 in a Django Web App
Project Structure
ObjectDetection/ # Django Project Root
│── detection/ # Django App
│ ├── migrations/ # Django Database Migrations
│ ├── static/ # Static Files (CSS, JS)
│ ├── templates/ # HTML Templates
│ │ ├── detection/ # App-specific templates
│ │ │ ├── index.html # Main UI Template
│ ├── __init__.py # App Init File
│ ├── admin.py # Django Admin Config (Optional)
│ ├── apps.py # App Configuration
│ ├── models.py # Database Models (Not Needed Here)
│ ├── tests.py # Unit Tests (Optional)
│ ├── urls.py # App URLs
│ ├── utils.py # Object Detection Logic (cv2 model)
│ ├── views.py # App Views
│── staticfiles/ # Stores Static Files
│ ├── img/ # Stores Uploaded and Processed Images
│ │ ├── img.jpeg # Uploaded Image
│ │ ├── img_out.jpeg # Processed Image with Bounding Boxes
│ ├── css/ # Stores Static Assets (CSS, JS)
│ │ ├── bootstrap.min.css # Bootstrap CSS (Optional)
│── ObjectDetection/ # Main Django Project Folder
│ ├── __init__.py # Project Init File
│ ├── asgi.py # ASGI Config (For Deployments)
│ ├── settings.py # Project Settings
│ ├── urls.py # Root URLs
│ ├── wsgi.py # WSGI Config (For Deployments)
│── manage.py # Django Management Script
│── coconames.txt # COCO Class Labels
│── frozen_inference_graph.pb # Pretrained Model Weights
│── ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt # Model Config
│── requirements.txt # Dependencies (Optional)
Installation
1. Clone the Repository
git clone https://github.com/tanvirsweb/Object-Detection-Website-using-Django-YoloV3.git cd ObjectDetectionWebsite
2. Create and Activate a Virtual Environment
python -m venv venv source venv/bin/activate # On Windows use: venv\Scripts\activate
3. Install Dependencies
pip install -r requirements.txt
4. Apply Migrations
python manage.py migrate
5. Run the Server
python manage.py runserver
Open your browser and go to http://127.0.0.1:8000/
Usage
- Upload an image via the web interface.
- The image will be processed using the SSD MobileNet model.
- Detected objects and the processed image will be displayed.
Model Details
- Model: SSD MobileNet V3
- Configuration File:
ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt
- Weights:
frozen_inference_graph.pb
- Labels:
coconames.txt
Troubleshooting
- If static files are not loading, run:
python manage.py collectstatic
- Ensure
media/
anduploads/
folders have write permissions. - If
NoReverseMatch
error occurs, checkurls.py
for correct view names.
License
This project is licensed under the MIT License.