Animals Classification

This project explores the application of convolutional neural networks (CNNs) for the task of animal image classification. I have curated a dataset of diverse animal images and employed transfer learning to fine-tune pre-trained models on our specific task. The goal is to achieve high classification accuracy and contribute to the field of computer vision.

Demo

https://huggingface.co/spaces/dielz/animals-classifier-demo

To Do

• Using a dataset with a total amount of 10000+ data
• Training and test accuracy of at least 95%
• The model was able to classify at least 3 different classes

Dataset

To support the define objectives, the dataset was obtained from kaggle with 12 different classes and 17000+ data. However, due to limited resources, only 10 classes were used in this project.

Model Evaluation

Training & Validation Accuracy

Training & Validation Loss

Confusion Matrix

Summary

Model	Structure	Data Splitting	Training Accuracy	Validation Accuracy
1	Sequential + MobileNetV2 + Conv2D + MaxPool2D	80/20	98%	95%

Prediction

Seen from the summary and prediction results of the model that has fulfilled all the points that are the objective of this project.