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---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
datasets:
- cifar10-lt
metrics:
- accuracy
- f1
model-index:
- name: cifar10-lt
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: cifar10-lt
type: cifar10-lt
config: r-10
split: test
args: r-10
metrics:
- name: Accuracy
type: accuracy
value: 0.9659
- name: F1
type: f1
value: 0.9660399066727052
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cifar10-lt
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the cifar10-lt dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1132
- Accuracy: 0.9659
- F1: 0.9660
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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