Horses or Humans Recognition Data

Keywords: Horses, or

Background

This study develops a CNN model for accurately classifying images as horses or humans. The CNN architecture extracts relevant features, enabling effective classification. The dataset is preprocessed and used to train the model, achieving promising accuracy in recognizing horses and humans. The research contributes to computer vision and has applications in image recognition and object classification. Choosing the appropriate model architecture, such as Convolutional Neural Networks (CNNs), and selecting optimal hyperparameters is challenging. Different architectures and hyperparameter settings may yield different results and trade-offs in terms of accuracy and computational efficiency. To develop a CNN model that accurately distinguishes between images of horses and humans. The goal is to train the model using the provided dataset to achieve high accuracy in classifying new images as either horses or humans. The Horses or Humans Recognition model using CNN achieved a training accuracy of 0.9684 and a validation accuracy of 0.9707. The model's training loss was 0.0979, and the validation loss was 0.0506. These results indicate the model's strong performance in accurately classifying horses and humans in images.

Aim & Objectives

Aim
To Recognize the Human or horses image classification Data using DL Techniques
Objective
Utilize an ANN model for accurate classification of human and horse images, aiding in diverse applications.
Develop an ANN system that analyzes image features to distinguish between human and horse subjects.
Evaluate the ANN's classification accuracy, ensuring reliable differentiation between human and horse images for various purposes.

Research Methodology

ANN Technique used

Software & Tools

Jupyter, Google Collab

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