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---
library_name: transformers
tags:
- vit
- cifar10
- image classification
license: apache-2.0
datasets:
- uoft-cs/cifar10
language:
- en
metrics:
- accuracy
- perplexity
pipeline_tag: image-classification
---
## Model Details
### Model Description
An adapter for the [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) ViT trained on CIFAR10 classification task
## Loading guide
```py
from transformers import AutoModelForImageClassification
labels2title = ['plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
model = AutoModelForImageClassification.from_pretrained(
'google/vit-base-patch16-224-in21k',
num_labels=len(labels2title),
id2label={i: c for i, c in enumerate(labels2title)},
label2id={c: i for i, c in enumerate(labels2title)}
)
model.load_adapter("yturkunov/cifar10_vit16_lora")
```
## Learning curves
### Recommendations to input
The model expects an image that has went through the following preprocessing stages:
* Scaling range:
<img src="https://latex.codecogs.com/gif.latex?[0, 255]\rightarrow[0, 1]" />
* Normalization parameters:
<img src="https://latex.codecogs.com/gif.latex?\mu=(.5,.5,.5),\sigma=(.5,.5,.5)" />
* Dimensions: 224x224
* Number of channels: 3
### Inference on 3x4 random sample
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