Pokemon Classifier Gen9 1025

🚀 Pokemon Classifier Gen9

A fine - tuned ViT model for classifying up to Gen9 (1025) Pokémon images, offering high - precision identification for Pokémon - themed applications.

🚀 Quick Start

This is a fine - tuned ViT (Vision Transformer) model for Pokémon image classification. The model is trained to classify upto Gen9 (1025) Pokémon images.

✨ Features

• Designed for image classification tasks, specifically for identifying Pokémon characters.
• Can be used for Pokémon - themed apps, educational projects, and Pokémon identification in images.
• Note: The model is not designed for general - purpose image classification.

📦 Installation

This section does not have specific installation steps in the original document, so it is skipped.

💻 Usage Examples

Basic Usage

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# Define the device
device = "cuda" if torch.cuda.is_available() else "cpu"

# Load the model and image processor
model_id = "skshmjn/Pokemon-classifier-gen9-1025"
model = ViTForImageClassification.from_pretrained(model_id).to(device)
image_processor = ViTImageProcessor.from_pretrained(model_id)

# Load and process an image
img = Image.open('test.jpg').convert("RGB")
inputs = image_processor(images=img, return_tensors='pt').to(device)

# Make predictions
outputs = model(**inputs)
predicted_id = outputs.logits.argmax(-1).item()
predicted_pokemon = model.config.id2label[predicted_id]

# Print predicted class
print(f"Predicted Pokémon Pokédex number: {predicted_id+1}")
print(f"Predicted Pokémon: {predicted_pokemon}")

📚 Documentation

Intended Use

This model is designed for image classification tasks, specifically for identifying Pokémon characters. It can be used for:

• Pokémon - themed apps
• Educational projects
• Pokémon identification in images

Note: The model is not designed for general - purpose image classification.

📄 License

This model is licensed under the apache - 2.0 license.