Overview

This repository showcases projects utilizing deep learning frameworks for image classification and object detection. These projects cover a range of applications, from recognizing handwritten digits to classifying wildlife.

Projects

Digit Recognition

• Dataset: Hand-drawn digit images from Kaggle.
• Achievement: Achieved 98.76% accuracy.
• Process: Developed a simple convolutional neural network (CNN).

Pet Classification

• Dataset: Cat and dog images from Kaggle.
• Achievements:
  • Self-constructed model: 82% accuracy.
  • Pre-trained model: 99% accuracy.
• Process: Developed two convolutional neural networks, one self-constructed and one leveraging a pre-trained model (e.g., ResNet, EfficientNet).

Plant Disease Detection

• Dataset: Plant leaf images from Kaggle.
• Achievement: [X]% accuracy.
• Process: Developed a convolutional neural network (CNN) to classify healthy and diseased plant leaves.

Wildlife Classification

• Dataset: A curated dataset of images featuring various wildlife species, collected from
• Achievement: [X]% accuracy.