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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cifar10
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-finetuned-cifar10
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: cifar10
type: cifar10
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9875
- task:
type: image-classification
name: Image Classification
dataset:
name: cifar10
type: cifar10
config: plain_text
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.973
verified: true
- name: Precision Macro
type: precision
value: 0.9734266055324291
verified: true
- name: Precision Micro
type: precision
value: 0.973
verified: true
- name: Precision Weighted
type: precision
value: 0.9734266055324291
verified: true
- name: Recall Macro
type: recall
value: 0.9730000000000001
verified: true
- name: Recall Micro
type: recall
value: 0.973
verified: true
- name: Recall Weighted
type: recall
value: 0.973
verified: true
- name: F1 Macro
type: f1
value: 0.9730140713232215
verified: true
- name: F1 Micro
type: f1
value: 0.973
verified: true
- name: F1 Weighted
type: f1
value: 0.9730140713232215
verified: true
- name: loss
type: loss
value: 0.09959099441766739
verified: true
---
<!-- 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. -->
# vit-base-patch16-224-in21k-finetuned-cifar10
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the cifar10 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0503
- Accuracy: 0.9875
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3118 | 1.0 | 1562 | 0.1135 | 0.9778 |
| 0.2717 | 2.0 | 3124 | 0.0619 | 0.9867 |
| 0.1964 | 3.0 | 4686 | 0.0503 | 0.9875 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.11.6
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