---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-finetuned-cassava3
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: image_folder
      type: image_folder
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8855140186915887
---

<!-- 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-cassava3

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 image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3419
- Accuracy: 0.8855

## 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: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5624        | 0.99  | 133  | 0.5866          | 0.8166   |
| 0.4717        | 1.99  | 266  | 0.4245          | 0.8692   |
| 0.4105        | 2.99  | 399  | 0.3708          | 0.8811   |
| 0.3753        | 3.99  | 532  | 0.3646          | 0.8787   |
| 0.2997        | 4.99  | 665  | 0.3655          | 0.8780   |
| 0.3176        | 5.99  | 798  | 0.3545          | 0.8822   |
| 0.2849        | 6.99  | 931  | 0.3441          | 0.8850   |
| 0.2931        | 7.99  | 1064 | 0.3419          | 0.8855   |
| 0.27          | 8.99  | 1197 | 0.3419          | 0.8848   |
| 0.2927        | 9.99  | 1330 | 0.3403          | 0.8853   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1