Pokemon Type Classification and Performance

Joseph Laurel presents a project on classifying Pokemon types using neural networks. The project aims to identify Pokemon types from images and suggest counters for threats. Data was collected from pokemondb.net, and models were trained using convolutional neural networks and transfer learning. The results show improved recall with advanced models. A web app demonstrates the model's predictions and suggests counters. Future improvements include training with diverse images and enhancing user interaction.

What is the maximum number of types a single Pokemon can have?

• Two

• Three

• Four

• One

What is the primary goal of the Pokemon type classification project?

• To eliminate type weaknesses

• To create new Pokemon types

• To increase the number of Pokemon species

• To determine Pokemon types from images

Which Pokemon type is the most prevalent in the dataset?

• Water

• Fire

• Ice

• Flying

Why is recall particularly important in the context of this project?

• It prevents Pokemon from fainting

• It ensures alerts are correct

• It helps in creating new Pokemon

• It increases the number of images

What is the main advantage of using transfer learning models like Inception V3?

• They are cheaper to run

• They learn faster and perform better

• They require no data

• They are easier to code

Which type of Pokemon had the highest recall in the model's performance?

• Ground

• Flying

• Fire

• Water

What is the purpose of the Flask web app in the project?

• To predict Pokemon types and suggest counters

• To create new Pokemon

• To train new models

• To delete Pokemon data

What additional step is suggested to improve the application?

• Adding more Pokemon types

• Increasing the app's size

• Training with more diverse images

• Reducing the number of counters

What does the web app display after predicting the Pokemon type?

• The Pokemon's evolution

• The Pokemon's habitat

• The top five counters

• The Pokemon's trainer

What is a potential improvement mentioned for the web app?

• Reducing the app's speed

• Increasing the number of predictions

• Simulating matchups

• Adding a chat feature