Instructions to use nathanReitinger/FASHION-vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nathanReitinger/FASHION-vision with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nathanReitinger/FASHION-vision") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("nathanReitinger/FASHION-vision") model = AutoModelForImageClassification.from_pretrained("nathanReitinger/FASHION-vision", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 473 Bytes
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license: apache-2.0
base_model: google/vit-huge-patch14-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: FASHION-vision
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# FASHION-vision
This model is a fine-tuned version of vit-huge-patch14-224-in21k on MNIST FASHION dataset.
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