Intel Natural Scene Classification using Deep Learning
This project is based on the Intel Image Classification dataset from Kaggle, which contains approximately 25,000 natural scene images categorized into six classes: buildings, forest, glacier, mountain, sea, and street. The dataset is designed for multi-class image classification tasks and is widely used for benchmarking computer vision and deep learning models.
Mentor Details:
Orna Bregman Amitai
Mentor Details:
Requirments:
- Develop a multi-class image classification model for natural scenes.
- Apply image preprocessing and data augmentation techniques.
- Train and evaluate convolutional neural networks (CNNs).
- Analyze model performance using accuracy and confusion matrices.
Problem Statement
Manual classification of natural scene images is time-consuming and subjective. Automated scene classification is essential for applications such as autonomous navigation, environmental monitoring, image retrieval, and smart city technologies. Robust AI models are required to accurately distinguish between visually similar scene categories.
Project Objectives
- Develop a multi-class image classification model for natural scenes.
- Apply image preprocessing and data augmentation techniques.
- Train and evaluate convolutional neural networks (CNNs).
- Analyze model performance using accuracy and confusion matrices.
Technical Scope
- Image loading, resizing, and normalization
- Data augmentation for improved generalization
- CNN-based multi-class classification
- Model evaluation and error analysis
- Optional use of transfer learning with pre-trained architectures
Required Knowledge and Prerequisites
Core Requirements:
- Python programming
- Machine learning and deep learning fundamentals
- Familiarity with TensorFlow or PyTorch
Recommended Background:
- Computer vision concepts
- Experience with CNN architectures
- Understanding of multi-class classification metrics
Project Difficulty and Expected Level
Overall Difficulty: Beginners
This project is well-suited for:
Teams of 1–3 students
Expected Outcomes
- A trained deep learning model for natural scene classification
- Quantitative evaluation results on test data
- A reproducible image classification pipeline
- Practical experience with real-world computer vision datasets