Instructions to use Enayat/ViT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Enayat/ViT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Enayat/ViT") 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("Enayat/ViT") model = AutoModelForImageClassification.from_pretrained("Enayat/ViT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 734 Bytes
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tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: ViT
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9299362897872925
---
# ViT
Created a Simple Image Classifier using ViT Transformers, custom trained on blur-dataset with an acc of about 93%.
The Dataset link : https://www.kaggle.com/datasets/kwentar/blur-dataset
## Architecture

## Example Images
#### defocused_blurred

#### motion_blurred

#### sharp
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