Instructions to use ghermoso/vit-eGTZANplus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-eGTZANplus
results: []
datasets:
- ghermoso/egtzan_plus
pipeline_tag: image-classification
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