This project showcases an implementation of object detection using TensorFlow's Object Detection API. It employs pre-trained deep learning models to detect and classify objects within images. The detected objects are highlighted with bounding boxes and labels, providing clear visual representation of the detection results.
The project is ideal for developers, researchers, and students interested in computer vision, artificial intelligence, and deep learning applications. It serves as a practical example of how to leverage TensorFlow’s powerful API for real-world object detection tasks.
- Utilizes TensorFlow’s efficient Object Detection API.
- Supports pre-trained models for detecting multiple object classes.
- Processes images to output annotated images showing detected objects.
- Includes utility functions for image loading, visualization, and result display.
- Modular and extensible to allow integration with video streams or other datasets.
- Ensure Python 3.x is installed on your system.
- Install TensorFlow and required libraries by running:
pip install tensorflow numpy pillow matplotlib
- Clone or download this repository to your local machine.
- Place your input images in a designated folder or specify the image path in the script.
- Run the
Object_detection_tensorflow.pyscript:
python Object_detection_tensorflow.py
- The script applies the object detection model to the input images and generates output visuals with bounding boxes and labels around detected objects.
- Python 3.x
- TensorFlow (compatible with TensorFlow Object Detection API)
- NumPy
- Pillow
- Matplotlib
The script loads a pre-trained TensorFlow model specifically trained for object detection tasks. It then processes input images by resizing and converting them into tensors suitable for the model. The model outputs bounding box coordinates, class labels, and confidence scores for each detected object. These results are then used to draw bounding boxes and labels on the images for visualization.
- Swap pre-trained models by changing the model checkpoint path.
- Adapt the code to process video streams or real-time camera input.
- Adjust detection threshold to filter out low confidence predictions.
- The TensorFlow module must be properly installed; otherwise, the script will not run.
- For advanced object detection tasks, consider training your own models or fine-tuning pre-trained ones.
- This project is a great starting point for AI applications involving object recognition.
This project is open source and available under the MIT License.