AI-ML-Classifiers

AI Multitask Classifiers: From Objects to Emotions

Created by Son Nguyen in 2024, this repository contains Python scripts for various AI-powered classifiers. These classifiers can be used for object detection, face detection, character recognition, and more. The classifiers are built using popular deep learning frameworks such as OpenCV, TensorFlow, and PyTorch.

This repository contains 9 subdirectories for the 9 classifiers:

Classifier Subdirectory Name
Vehicle Classification Vehicle-Classification
Human Face Classification Human-Face-Classification
Mood Classification Mood-Classification
Flower Classification Flowers-Classification
Object Classification Object-Classification
Character Recognition Character-Recognition
Animal Classification Animals-Classification
Speech Recognition Speech-Recognition
Sentiment Analysis Sentiment-Analysis

Detailed information about each classifier can be found below.

What’s even more interesting is that all these classifiers can use your webcam for live testing, video files, or image files!

Please read this README file carefully to understand how to use each classifier and how to run the main script to choose and run any of the classifiers. Happy classifying! 🚀

Table of Contents

Before You Begin

Before you begin, ensure you have the following installed on your machine (run pip install <requirement_name> for each dependency or pip install -r requirements.txt to install all the required packages):

It is also recommended to use a virtual environment to use these classifiers. You can create a virtual environment using venv or conda:

python -m venv env
source env/bin/activate

Main Script

If you prefer not to navigate through the subdirectories, you can run the main script main.py to choose and run any of the classifiers. The main script will ask you to choose a classifier from the list of available classifiers. You can then select a classifier and run it.

To run the main script, use the following command:

python main.py

The main script will display a list of available classifiers. Enter the number corresponding to the classifier you want to run. The script will then run the selected classifier.

To stop the script, press Q, ESC, or otherwise close the window.

Alternatively, you can also run the individual scripts in each subdirectory below to run the classifiers directly.

Flask Web App

If you would like to use the interactive website version of this app, you can run the Flask web app. The web app allows you to use the classifiers through a web interface. You can choose a classifier and the app will run the selected classifier.

To run the Flask web app, use the following command:

python app.py

The web app will start running on http://127.0.0.1:5000/. Open this URL in your web browser to access the web app. You can then choose a classifier from the list of available classifiers and run it. A pop-up window will display the output of the classifier - so be sure to allow pop-ups in your browser.

Here is what it looks like:

Flask Web App

Note that the app has also been deployed to Heroku at this link. However, due to changes in Heroku’s free tier regarding available Dynos (and I’m a broke college student), the app may not work as expected. If you encounter any issues, please run the app locally using the instructions above.

Vehicle Classification

Files Included

Getting Started

  1. Clone the Repository

    git clone https://github.com/hoangsonww/AI-Classification.git
cd AI-Classification/Vehicle-Classification

  2. Download Model Weights Download the pre-trained YOLOv3 model weights (yolov3.weights) from the official YOLO website or another trusted source and place it in the Vehicle-Classification directory.

  3. Install Dependencies Install the required Python dependencies.

    pip install -r requirements.txt

  4. Install and Pull Git LFS Install Git LFS by following the instructions on the official Git LFS website. Then, pull the model weights using Git LFS.

    git lfs install
git lfs pull

    Alternatively, you can download the weights file from the official YOLO website and place it in the Vehicle-Classification directory. However, using Git LFS is recommended.

    This is crucial for pulling the large model weights file yolov3.weights. Without Git LFS, the weights file may not be downloaded correctly and the script may not work as expected.

    If you still encounter problems with Git LFS, you can download the weights file from my Google Drive, which is publicly available here.

  5. Run Vehicle Detection

    python vehicle_detection.py

You will then be asked to choose your input type (image, video, or webcam). Enter image to classify the vehicles in the sample video provided (traffic.mp4), or enter video to classify vehicles in a video file. You can also use your webcam for live testing.

All our classifiers will only stop when you press Q, ESC, or otherwise close the window.

Output

The output video will display the detected vehicles along with their class labels. The class labels are based on the COCO dataset, which includes various classes such as car, truck, bus, motorcycle, and bicycle.

Example output:

Vehicle Classification Output

Face Classification

Files Included

Getting Started

  1. Clone the Repository

    git clone https://github.com/hoangsonww/AI-Classification.git
cd AI-Classification/Face-Classification

  2. Download Model Weights: Ensure you have the model weights (res10_300x300_ssd_iter_140000.caffemodel, age_net.caffemodel, gender_net.caffemodel) in the Human-Face-Classification directory.

  3. Install Dependencies: Install the required Python dependencies.

    pip install -r requirements.txt

  4. Run Face Classification: Execute the face_classification.py script.

    python face_classification.py

You will then be asked to choose your input type (image, video, or webcam). Enter image to classify the faces in the sample image provided (woman-30.mp4), or enter video to classify faces in a video file. You can also use your webcam for live testing.

All our classifiers will only stop when you press Q, ESC, or otherwise close the window.

Output

The output will be a video displaying the detected faces along with their estimated age and gender.

Example output:

Face Classification Output

Mood Classification

Files Included

Getting Started

  1. Clone the Repository 
    git clone
cd AI-Classification/Mood-Classification
  2. Install Dependencies Install the required Python dependencies. 
    pip install -r requirements.txt
  3. Run Mood Classification 
    python mood_classifier.py

You will then be asked to choose your input type (image, video, or webcam). Enter image to classify the mood in the sample image provided (surprised.jpg), or enter video to classify the mood in a video file. You can also use your webcam for live testing.

The script will then display the detected mood in the image, video, or webcam stream and in the console.

All our classifiers will only stop when you press Q, ESC, or otherwise close the window.

Output

The output will be displayed the detected mood in the image, video, or webcam stream and in the console.

Example output:

Mood Classification Output

Character Classification

Files Included

Getting Started

  1. Clone the Repository 
    git clone
cd AI-Classification/Character-Recognition
  2. Install the required Python dependencies. 
    pip install -r requirements.txt
  3. Install Tessaract OCR
    • For Windows: Download and install the Tesseract OCR executable from the official Tesseract OCR website.
    • For Linux: Install Tesseract OCR using the package manager. 
      sudo apt-get install tesseract-ocr
    • For macOS: Install Tesseract OCR using Homebrew. 
      brew install tesseract
    • This is required for the OCR functionality to work. Also, when you install, note down the installation path of the Tesseract OCR executable. Replace the path in the pytesseract.pytesseract.tesseract_cmd variable in the ocr.py script with yours.
    • For example, if you installed Tesseract OCR in the default location on Windows, the path would be: 
      pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'

  4. Run Character Classification

    python ocr.py

    You will then be asked to choose your input type (image, video, or webcam). Enter image to classify the characters in the sample image provided (OIP.jpg), or enter video to classify characters in a video file. You can also use your webcam for live testing.

    image

The script will then display the detected characters in the image, video, or webcam stream.

All our classifiers will only stop when you press Q, ESC, or otherwise close the window.

Output

The output will display the class labels of the characters detected in the image along with the confidence scores.

Example output:

Character Classification Output

Flower Classification

Files Included

Getting Started

  1. Clone the Repository

    git clone https://github.com/hoangsonww/AI-Classification.git
cd AI-Classification/Flowers-Classification

  2. Install Dependencies Install the required Python dependencies.

    pip install -r requirements.txt

  3. Run Object Classification

    python flower_classification.py

You will then be asked to choose your input type (image, video, or webcam). Enter image to classify the flowers in the sample image provided (daisy.jpg), or enter video to classify flowers in a video file. You can also use your webcam for live testing.

All our classifiers will only stop when you press Q, ESC, or otherwise close the window.

Output

The output will display the class label of the flower detected in the image along with the confidence score.

Example output: Here are the sample image of Daisy flowers.

Flower Classification Output

Object Classification

Files Included

Getting Started

  1. Clone the Repository

    git clone https://github.com/hoangsonww/AI-Classification.git
cd AI-Classification/object_classification

  2. Install Dependencies Install the required Python dependencies.

    pip install -r requirements.txt

  3. Run Object Classification

    python object_classification.py

You will then be asked to choose your input type (image, video, or webcam). Enter image to classify the objects in the sample image provided (objects.jpg), or enter video to classify objects in a video file. You can also use your webcam for live testing.

Feel free to change the paths and other parameters in the script to suit your needs.

Note: All our classifiers will only stop when you press Q, ESC, or otherwise close the window.

Output

The output will display the class labels of the objects detected in the image along with the confidence scores. Or, if you choose to use your webcam, the output will display the class labels of the objects detected in the video stream. If you choose to use a video file, the output will be a video displaying the detected objects along with their class labels.

Example output:

Object Classification Output

Animal Classification

Files Included

Getting Started

  1. Clone the Repository

    git clone https://github.com/hoangsonww/AI-Classification.git
cd AI-Classification/Animals-Classification

  2. Install Dependencies Install the required Python dependencies.

    pip install -r requirements.txt

  3. Run Object Classification

    python animal_classification.py

The script will then ask you to choose your input type (image, video, or webcam). Enter image to classify the animals in the sample image provided (cow.jpg), or enter video to classify animals in a video file. You can also use your webcam for live testing.

All our classifiers will only stop when you press Q, ESC, or otherwise close the window.

Output

The output will display the class labels of the animals detected in the image along with the confidence scores.

Example output:

Animal Classification Output

Speech Recognition

Files Included

Getting Started

  1. Clone the Repository

    git clone https://github.com/hoangsonww/AI-Classification.git
cd AI-Classification/Speech-Recognition
  2. Install Dependencies Install the required Python dependencies. 
    pip install -r requirements.txt
  3. Run Speech Recognition 
     python speech_classifier.py

You will then be asked to choose your preferred input method (microphone or video). Enter microphone to use your microphone for live speech recognition, or enter video to use a video file for speech recognition.

Output

You will see the output of the speech recognition process in the console. The script will display the recognized speech from the audio input. The audio is processed in chunks and recognized in real-time. All our classifiers will stop when you press Q, ESC, or otherwise close the window.

Example output:

Speech Recognition Output

Special: Self-Trained Sentiment Classifier

In addition to the other pre-trained classifiers, this repository includes a special sentiment classifier that you can train yourself. The sentiment classifier is trained on a large dataset of tweets and can classify the sentiment of a sentence as positive, negative, or neutral. This is excellent for educational purposes and for understanding how sentiment analysis works.

Files Included

Getting Started

  1. Clone the Repository 
    git clone
cd AI-Classification/Sentiment-Analysis
  2. Install Dependencies: Install the required Python dependencies. 
    pip install scikit-learn pandas numpy nltk tqdm joblib

  3. Pull the Large Training Data: The sentiment classifier is trained on a large dataset of tweets. The large training data is stored in a CSV file named training.1600000.processed.noemoticon.csv. This file is stored using Git LFS due to its large size. To pull the large training data, use the following command:

    git lfs install
git lfs pull

  4. Train the Sentiment Classifier Run the train_model.py script to train the sentiment classifier.

    python train_model.py
    • When running the script, you will be asked to choose the dataset size (small or large). Enter small to use the small dataset or large to use the large dataset. The script will then preprocess the training data, train the sentiment classifier, and save the trained model and vectorizer to disk.
    • However, if you choose small, the script will use the small dataset provided in the repository. In order to use it, be sure to run the generate_small_dataset.py script first to generate the small dataset from the large training data.
    python generate_small_dataset.py
    • Note: Training the sentiment classifier on the large dataset may take a long time and require significant computational resources. However, it is recommended since it provides better model accuracy.
    • Once again, if you are patient and have a good machine, you are encouraged use the large dataset to get a higher accuracy. Otherwise, use the small dataset for faster training.

    • This script will then preprocess the training data, train the sentiment classifier, and save the trained model and vectorizer to disk.

    • Additionally, it will output the expected accuracy, F1 score, and expected confidence level of the sentiment classifier. The higher these statistics are, the better the sentiment classifier will perform. Of course, this is highly dependent on the training dataset size and quality. Feel free to experiment with the training data and parameters to improve the sentiment classifier’s performance.
  5. Run Sentiment Classification 
    python sentiment_classifier.py

    You will then be asked to enter a sentence for sentiment classification. Enter a sentence, and the script will classify the sentiment of the sentence as positive, negative, or neutral, with a level of confidence.

Output

The output will display the sentiment classification of the input sentence. The sentiment classifier will classify the sentiment as positive, negative, or neutral.

Training Output Example:

Sentiment Classifier Training Output

Classification Output Example:

Sentiment Classifier Classification Output

Feel free to experiment with the sentiment classifier and test it with your own sentences and explore how powerful sentiment analysis can be!

Containerization

If you would like to containerize this application, you can use Docker to create a container image. The Dockerfile is included in the repository for this purpose. To build the Docker image, use the following command:

docker build -t ai-multitask-classifiers .

This command will build a Docker image named ai-multitask-classifiers based on the Dockerfile in the repository. You can then run the Docker container using the following command:

docker run -p 5000:5000 ai-multitask-classifiers

This command will run the Docker container and expose port 5000 for the Flask web app. You can access the web app by visiting http://127.0.0.1:500.

Note: Before containerizing the application, ensure you have Docker installed on your machine. You can download Docker from the official Docker website. Then, ensure that Docker Desktop is running on your machine before building and running the Docker container.

Contact Information

For any questions or issues, please refer to the contact information below:

Future Work

License

This project is licensed under the MIT License - see the LICENSE file for details.

Live Information Website

Feel free to visit the live demo and information website here.

It is a simple website that provides information about the classifiers in this repository.

Created with ❤️ by Son Nguyen in 2024.

Thank you for visiting! 🚀

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