Details

This project aims to develop an image classification model using TensorFlow to distinguish between three main categories: rock, paper, and scissors. Firstly, a representative dataset for each category will be collected and systematically organized. Then, data preprocessing will be conducted to ensure the consistency and cleanliness of the dataset.

The model development process will begin with the construction of a neural network architecture using TensorFlow. This model will consist of convolutional layers to extract important features from the images, followed by fully connected layers for classification. Appropriate activation functions and techniques such as dropout will be implemented to enhance performance and reduce overfitting.

Furthermore, the dataset will be divided into training and validation sets to evaluate the model's performance during training. The training process will involve parameter optimization through gradient-based learning algorithms using the appropriate loss function. Once the model achieves satisfactory accuracy on the validation set, further testing will be conducted using a separate test dataset.

During the testing phase, the model will be evaluated based on multiple metrics, including accuracy, precision, recall, and F1 score. Error analysis will also be performed to understand areas where the model can be further improved. Finally, the trained model will be implemented in an application or user interface that allows users to test the model with their own rock, paper, and scissors images.