Edit model card

Model Details

Model Description

An adapter for the google/vit-base-patch16-224 ViT trained on CIFAR10 classification task

Loading guide

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

image/png

Recommendations to input

The model expects an image that has went through the following preprocessing stages:

  • Scaling range:
  • Normalization parameters:
  • Dimensions: 224x224
  • Number of channels: 3

Inference on 3x4 random sample

image/png

Downloads last month

-

Downloads are not tracked for this model. How to track
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Dataset used to train yturkunov/cifar10_vit16_lora