Tensorflow Object Detection in 5 Hours with Python | Full Course with 3 Projects

Introduction

This tutorial will guide you through the process of building deep learning object detection models using TensorFlow and Python. By the end of this tutorial, you will be equipped to create your own models for various applications, including gesture detection, defect detection, and sentiment analysis.

Step 1: Installation and Setup

1. Install TensorFlow Object Detection

   • Follow the official installation guide to install TensorFlow on your local machine or Google Colab.
   • Ensure you have Python and pip installed on your system.

2. Clone the Baseline Code from GitHub

   • Use the following command to clone the repository:

     git clone https://github.com/nicknochnack/TFODCourse
     

3. Create a Virtual Environment

   • Set up a virtual environment to manage dependencies:

     python -m venv tfod-env
     

   • Activate the environment:
     • On Windows:

       tfod-env\Scripts\activate
       

     • On macOS/Linux:

       source tfod-env/bin/activate
       

Step 2: Collecting Images and Labeling

1. Collect Images Using Your Webcam

   • Use OpenCV or similar libraries to capture images from your webcam for training.

2. Label Images for Object Detection

   • Download and install LabelImg.
   • Use LabelImg to annotate your images, saving the labels in the desired format (Pascal VOC or YOLO).

Step 3: Training TensorFlow Object Detection Models

1. Access TensorFlow Model Zoo

   • Explore pre-trained models available in the TensorFlow Model Zoo that can be fine-tuned for your tasks.

2. Install TensorFlow Object Detection API

   • Follow the official TensorFlow Object Detection API installation instructions.

3. Install CUDA and cuDNN

   • Install CUDA and cuDNN if you plan to use GPU acceleration.

4. Create and Update a Label Map

   • Create a label map file that maps class IDs to class names.

5. Create TF Records

   • Convert your dataset into TF Record format for model training.

6. Train Object Detection Models

   • Use the following command to start training:

     python model_main_tf2.py --model_dir=your_model_directory --pipeline_config_path=your_pipeline.config
     

7. Evaluate Models

   • Assess the model's performance using metrics like Precision and Recall, and visualize results with TensorBoard.

Step 4: Detecting Objects from Images and Webcams

1. Detect Objects in Images

   • Use the trained model to perform object detection on static images.

2. Detect Objects in Real Time

   • Implement real-time object detection using a webcam with the following code snippet:

   import cv2
   # Load your model and start capturing video
   

Step 5: Freezing TensorFlow Graph and Converting Models

1. Freeze the TensorFlow Graph

   • Freeze your model for deployment purposes.

2. Convert to TensorFlow JS

   • Use TensorFlow.js converter to prepare the model for web applications:

     tensorflowjs_converter --input_format=tf_saved_model --output_format=tfjs_graph_model /path/to/saved_model /path/to/web_model
     

3. Convert to TFLite

   • Convert the model to TensorFlow Lite for use on mobile or embedded devices:

     tflite_convert --saved_model_dir=/path/to/saved_model --output_file=/path/to/model.tflite
     

Step 6: Performance Tuning

1. Tune Object Detection Models
   • Adjust hyperparameters and retrain your model to improve metrics like Precision and Recall.

Step 7: Object Detection Projects

1. Project 1: Microscope Defect Detection

   • Build a model using USB microscope images to detect defects in electronics.

2. Project 2: Web Direction Detection

   • Create a web app using React.js to detect hand directions with TensorFlow.js.

3. Project 3: Sentiment Detection on Raspberry Pi

   • Implement a face sentiment detection model on a Raspberry Pi using TFLite.

Conclusion

By following these steps, you should have a solid foundation in TensorFlow object detection, allowing you to create different models for various applications. For further exploration, consider diving into the projects mentioned and experimenting with different datasets and models. Happy coding!