This project focuses on the development of an advanced image classification system. The goal is to accurately categorize images into predefined classes based on their content.
Key Components
- Convolutional Neural Networks (CNNs): The core technology used for image recognition.
- Pre-trained Models: Utilizing pre-trained models like ResNet or Inception for faster and more accurate results.
- Custom Layers: Developing custom layers for specific project requirements.
Project Goals
- High Accuracy: Achieve an accuracy rate of 95% or higher on the test dataset.
- Real-time Processing: Ensure the system can process images in real-time.
- Robustness: The system should be able to handle variations in lighting, angles, and other factors.
Dataset
The project utilizes a diverse dataset of over 1 million images across various categories. The dataset is split into training, validation, and test sets.
Challenges
- Data Augmentation: Enhancing the dataset with techniques like rotation, scaling, and flipping to improve model generalization.
- Overfitting: Implementing regularization techniques to prevent the model from overfitting to the training data.
Next Steps
- Training the Model: Start the training process using the prepared dataset.
- Evaluation: Evaluate the model's performance on the validation set.
- Fine-tuning: Adjust the model parameters based on the evaluation results.
For more information on image classification techniques, check out our Introduction to Image Classification.