Emoji-based-Emotion-prediction

Description

This project uses emoji photos to predict their sentiments. To achieve the intended outcome, transfer learning was employed to train the MobileNetV2 model.

Dependencies

1. python3.8 or greater version
2. Tensorflow2.8
3. numpy
4. matplotlib

Dataset Details

The data collection contains 168 different categories of images. They are classified as smiling, sad, or angry. A directory data contains three directories with the class label's name. These labels include the images that correspond to them.

Working

• The dependencies are imported.
• The data generators are setup for training and validation data.
• The ImageDataGenerator is configured with various data augmentation techniques such as rotation, width and height shift, and horizontal flip. It divides the data into subsets for training and validation.
• A model is constructed by combining the MobileNetV2 base model with additional layers for customization.
• The model is trained to predict the label.

Training a Custom Model

To train a custom model put the images folder into the data directory where each folder will contain images with their respective names. After this run the provided jupyter notebook, emoji_emotion_recog_jupyter.ipynb, the model will be trained and saved into the model folder.

Getting Started

• Clone the repository in your terminal git clone https://github.com/akshitagupta15june/Face-X.git
• Switch directory cd Emoji-based-Emotion-prediction
• Run file emoji_emotion_recog_jupyter.ipynb

Want to Contribute?

If you wish to contribute refer CONTRIBUTING.md: https://github.com/akshitagupta15june/Face-X/blob/master/CONTRIBUTING.md

Result