1. Setup and Imports:

   • You import required libraries like TensorFlow, TensorFlow Hub, and others for image processing and visualization.

2. Utility Functions:

   • download_and_resize_image: Downloads an image, resizes it, and saves it locally.
   • display_image: Displays an image using Matplotlib.
   • draw_bounding_box_on_image and draw_boxes: Functions for drawing bounding boxes around detected objects.

3. Object Detection:

   • You load the Faster R-CNN object detection model using TensorFlow Hub.
   • You download a sample image of a kangaroo and process it.
   • The run_detector function handles model inference, outputs detected objects, and overlays bounding boxes with class names and scores.

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Object Detection Using TensorFlow Hub

Overview

This project demonstrates object detection using TensorFlow and TensorFlow Hub. The Faster R-CNN model pre-trained on the Open Images V4 dataset is used to detect objects in images. The project includes visualization of detected objects with bounding boxes and class labels.

Features

• Model: Faster R-CNN (Open Images V4 + Inception ResNet V2)
• Framework: TensorFlow and TensorFlow Hub
• Utilities:
  • Download and resize an image
  • Visualize object detection results
  • Annotate images with bounding boxes and class labels

Requirements

• Python 3.6+
• TensorFlow 2.x
• TensorFlow Hub
• NumPy
• Matplotlib
• Pillow ository-folder>

2. Install dependencies:

   pip install tensorflow tensorflow-hub numpy matplotlib pillow

Usage

1. Download or provide an image URL.
2. Run the detection script:

   python object_detection.py

Example Output

Input Image:

A kangaroo image from Wikimedia Commons.

Detected Output:

Objects detected in the image, annotated with bounding boxes and labels.

References

• TensorFlow Hub: Faster R-CNN
• TensorFlow Object Detection API