Object Detection With Gradio

What is Object Detection With Gradio ?

Object Detection With Gradio is a web-based application designed to detect objects in images and videos. Built using Gradio, an open-source library for creating machine learning demos, this tool provides an interactive interface for users to upload media and receive object detection results. It leverages AI models to identify and classify objects within the uploaded content, making it a powerful tool for both developers and non-technical users.

Features

• Real-Time Object Detection: Detect objects in images and videos with precise bounding boxes and class labels.
• Support for Multiple Formats: Process both images (e.g., JPG, PNG) and video files (e.g., MP4, AVI).
• Customizable Models: Use pre-trained AI models or integrate custom models for specific use cases.
• User-Friendly Interface: A simple and intuitive web interface for uploading files and viewing results.
• Integration with Gradio: Leverage Gradio's capabilities for seamless deployment and sharing of the application.

How to use Object Detection With Gradio ?

1. Install Gradio: Ensure you have Gradio installed in your environment by running pip install gradio.
2. Run the Application: Launch the Object Detection app using Gradio's interface.
3. Upload Media: Click the input button to upload an image or video file.
4. Process Detection: Click the Submit or Predict button to run the detection process.
5. View Results: Review the output showing detected objects with bounding boxes and confidence scores.

Frequently Asked Questions

What is Gradio?
Gradio is an open-source Python library that allows users to create shareable, web-based interfaces for machine learning models. It simplifies the process of deploying and testing AI applications.

What file formats are supported?
The application supports common image formats like JPG, PNG, and BMP, as well as video formats such as MP4, AVI, and MOV.

How do I change the confidence threshold for object detection?
You can adjust the confidence threshold by modifying the model parameters in the code. Lowering the threshold increases the number of detections but may reduce accuracy.