---
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: 0.8705607476635514
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# 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: 0.3742
- Accuracy: 0.8706

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5628        | 1.0   | 150  | 0.5357          | 0.8308   |
| 0.4398        | 2.0   | 300  | 0.4311          | 0.8598   |
| 0.4022        | 3.0   | 450  | 0.3958          | 0.8668   |
| 0.3855        | 4.0   | 600  | 0.4030          | 0.8598   |
| 0.3659        | 5.0   | 750  | 0.4125          | 0.8617   |
| 0.3393        | 6.0   | 900  | 0.3840          | 0.8673   |
| 0.3022        | 7.0   | 1050 | 0.3775          | 0.8673   |
| 0.2941        | 8.0   | 1200 | 0.3742          | 0.8706   |
| 0.2903        | 9.0   | 1350 | 0.3809          | 0.8696   |
| 0.2584        | 10.0  | 1500 | 0.3756          | 0.8696   |


### Framework versions

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