# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("imjeffhi/pokemon_classifier")
model = AutoModelForImageClassification.from_pretrained("imjeffhi/pokemon_classifier")Quick Links
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Check out the documentation for more information.
Pokémon Classifier
Intro
A fine-tuned version of ViT-base on a collected set of Pokémon images. You can read more about the model here.
Using the model
from transformers import ViTForImageClassification, ViTFeatureExtractor
from PIL import Image
import torch
# Loading in Model
device = "cuda" if torch.cuda.is_available() else "cpu"
model = ViTForImageClassification.from_pretrained( "imjeffhi/pokemon_classifier").to(device)
feature_extractor = ViTFeatureExtractor.from_pretrained('imjeffhi/pokemon_classifier')
# Caling the model on a test image
img = Image.open('test.jpg')
extracted = feature_extractor(images=img, return_tensors='pt').to(device)
predicted_id = model(**extracted).logits.argmax(-1).item()
predicted_pokemon = model.config.id2label[predicted_id]
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="imjeffhi/pokemon_classifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")