This repository contains code for identifying Simpsons characters using a neural network model. The dataset used for training and testing the model is sourced from Kaggle's "The Simpsons Characters Dataset".
The dataset consists of images of various Simpsons characters, each labeled with the corresponding character's name. It encompasses a wide range of characters from the Simpsons universe.
The identification task is approached using a neural network model implemented with PyTorch. The model architecture follows a sequential approach and includes convolutional layers, max-pooling layers, dropout layers, and fully connected layers for classification.
The sequential neural network architecture is structured as follows:
- Convolutional Layers: The model starts with a convolutional layer (
nn.Conv2d) followed by a rectified linear unit (ReLU) activation function (nn.ReLU). Max-pooling layers (nn.MaxPool2d) are used to downsample the feature maps. - Dropout: Dropout layers (
nn.Dropout) are introduced to prevent overfitting by randomly setting a fraction of input units to zero during training. - Flatten: After the convolutional layers, the output is flattened using
nn.Flatten()to prepare for the fully connected layers. - Fully Connected Layers: The flattened output is passed through one or more fully connected layers (
nn.Linear) with ReLU activation functions.
After training the model on the dataset, it achieved an accuracy of approximately 56% on the test set. While this accuracy may not be exceptionally high, it demonstrates the model's capability to recognize Simpsons characters to a certain extent. Further enhancements to the model architecture, data preprocessing, and training strategy could potentially improve its performance.
- The dataset used in this project is sourced from Kaggle, specifically from The Simpsons Characters Dataset.
- Inspiration and guidance for building the neural network model were drawn from various online tutorials and resources.