This project is our final assignment for the course Science, Technology, and Society in Prehistoric China. We attempt to use Convolutional Neural Networks (CNN) to distinguish different types of bronze artifacts.
The study of bronze artifacts provides invaluable insights into the technological advancements and social structures of ancient China. Our project focuses on leveraging modern machine learning techniques to classify various types of bronze artifacts, thus aiding archaeologists and historians in their research.
/The dataset used for this project includes high-resolution images of different types of bronze artifacts. These images were collected from various archaeological databases and publications. Each image is labeled according to the artifact type./
We utilized a Convolutional Neural Network (CNN) for the classification task. The CNN architecture was chosen due to its proven effectiveness in image recognition tasks. The model was trained using TensorFlow and Keras libraries.
Key steps in our methodology include:
- Data Preprocessing: Resizing images, normalization, and augmentation.
- Model Architecture: Designing and implementing the CNN model.
- Training: Training the model on the dataset with appropriate hyperparameters.
- Evaluation: Assessing the model's performance using metrics such as accuracy, precision, and recall.
We welcome contributions to improve this project. If you have any suggestions or bug reports, please open an issue or submit a pull request.
/The CNN model achieved an accuracy of XX% on the test dataset. The results indicate that the model is effective in distinguishing between different types of bronze artifacts. Detailed results and analysis can be found in the report [link to the report]./
MIT