ghermoso/egtzan_plus
Viewer • Updated • 1.89k • 108
How to use ghermoso/vit-eGTZANplus with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="ghermoso/vit-eGTZANplus")
pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png") # Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("ghermoso/vit-eGTZANplus")
model = AutoModelForImageClassification.from_pretrained("ghermoso/vit-eGTZANplus")Model Name: ghermoso/vit-eGTZANplus
Task: Image Classification
Dataset: egtzan_plus
Model Architecture: Vision Transformer (ViT)
Finetuned from model: This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an egtzan_plus dataset.
It achieves the following results on the evaluation set:
Base model
google/vit-base-patch16-224-in21k