# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("ghermoso/vit-eGTZANplus")
model = AutoModelForImageClassification.from_pretrained("ghermoso/vit-eGTZANplus")Quick Links
Vision Transformer (ViT) for Music Genre Classification
Model Overview
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:
- Loss: 0.8358
- Accuracy: 0.7460
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Model tree for ghermoso/vit-eGTZANplus
Base model
google/vit-base-patch16-224-in21k
# 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")