---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cifar10
metrics:
- accuracy
model-index:
- name: AML_A2_Q4
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: cifar10
      type: cifar10
      config: plain_text
      split: train[:]
      args: plain_text
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9894
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# AML_A2_Q4

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: 0.0432
- Accuracy: 0.9894

## 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: 2e-05
- train_batch_size: 20
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1642        | 1.0   | 2250 | 0.0572          | 0.9862   |
| 0.1503        | 2.0   | 4500 | 0.0591          | 0.9854   |
| 0.1818        | 3.0   | 6750 | 0.0432          | 0.9894   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3