---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-finetuned-cassava
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: image_folder
      type: image_folder
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8761682242990654
---

<!-- 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-cassava

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: 0.3875
- Accuracy: 0.8762

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5531        | 1.0   | 535  | 0.4938          | 0.8336   |
| 0.4139        | 2.0   | 1070 | 0.4071          | 0.8612   |
| 0.287         | 3.0   | 1605 | 0.3954          | 0.8643   |
| 0.4211        | 4.0   | 2140 | 0.3906          | 0.8701   |
| 0.316         | 5.0   | 2675 | 0.3716          | 0.8755   |
| 0.2709        | 6.0   | 3210 | 0.3784          | 0.8736   |
| 0.177         | 7.0   | 3745 | 0.3772          | 0.8745   |
| 0.2409        | 8.0   | 4280 | 0.3875          | 0.8762   |
| 0.1929        | 9.0   | 4815 | 0.3915          | 0.8708   |
| 0.1877        | 10.0  | 5350 | 0.3881          | 0.8715   |


### Framework versions

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