This project is an Image Classifier built using deep learning techniques, primarily utilizing PyTorch. The model is designed to classify images into specific categories and can be extended to various real-world applications like healthcare, education, and retail. The classifier is trained on a labeled dataset and is capable of making accurate predictions on new, unseen images.
- Image Classification: Classifies images into predefined categories.
- Model Training: Uses Convolutional Neural Networks (CNNs) for image classification.
- Performance Evaluation: Includes accuracy, precision, recall, and F1-score evaluation metrics.
- Deployment-ready: Can be easily deployed as a service for real-time predictions.
- Programming Language: Python
- Deep Learning Framework: PyTorch
- Libraries: NumPy, Pandas, Matplotlib, Seaborn
- The model is trained using a publicly available dataset (or custom dataset) for classification.
- The dataset is split into training, validation, and test sets for evaluation.