---
language: en
tags:
- vision
- image-classification
- pokemon
- pytorch
- transformers
license: apache-2.0
datasets:
- custom
pipeline_tag: image-classification
model_name: Pokemon Classifier Gen9
model_id: skshmjn/Pokemon-classifier-gen9-1025
library_name: transformers
framework: PyTorch
widget:
- src: test.jpg
metrics:
- accuracy
base_model:
- google/vit-base-patch16-224-in21k
---

# Model Card for Pokemon Classifier Gen9

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

## 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.

## How to Use

Here's how you can load and use the model with the Hugging Face `transformers` library:

```python
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}")