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Jupyter notebook showing how to build an image classifier with Python and Tensorflow

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Face_Detection_Model_using_Tensorflow

Deep Image Classifier: Happy or Sad

This repository contains a deep image classifier that can determine whether an image portrays a happy or sad emotion. The classifier is built using convolutional neural networks (CNN) and implemented using the TensorFlow framework.

Overview

The goal of this project is to develop a model that can accurately classify images based on the emotions they depict. The classifier has been trained on a large dataset of labeled happy and sad images, enabling it to learn the distinguishing features of each emotion.

Model Architecture

The deep image classifier utilizes a CNN architecture, which is well-suited for image classification tasks. It consists of multiple convolutional layers followed by pooling layers for feature extraction. The extracted features are then flattened and fed into fully connected layers, leading to the final classification output.

Usage

To use the image classifier, simply provide an image as input, and the model will predict whether the emotion portrayed in the image is happy or sad. The project provides a Python script that loads the trained model and performs the classification. Detailed instructions can be found in the project documentation.

Dataset

The classifier has been trained on a carefully curated dataset of diverse happy and sad images. The dataset is annotated with labels indicating the corresponding emotions, ensuring the model learns to differentiate between the two emotions effectively.

Performance and Evaluation

The classifier's performance has been evaluated using various metrics such as accuracy, precision, recall, and F1 score. Extensive testing has been conducted on both the training and validation datasets to assess the model's ability to generalize to unseen images.

Future Improvements

This project can be further enhanced in several ways. Some potential areas of improvement include exploring more advanced CNN architectures, augmenting the dataset with additional labeled images, and fine-tuning the model to improve its performance on specific subsets of emotions.

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Jupyter notebook showing how to build an image classifier with Python and Tensorflow

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