---
license: apache-2.0
tags:
- image-classification
- beans-classification
- generated_from_trainer
metrics:
- accuracy
widget:
- src: https://huggingface.co/jolual2747/vit-model-jose-alcocer/resolve/main/healthy.jpeg
  example_title: Healthy
- src: https://huggingface.co/jolual2747/vit-model-jose-alcocer/resolve/main/bean_rust.jpeg
  example_title: Bean Rust
base_model: google/vit-base-patch16-224-in21k
model-index:
- name: vit-model-jose-alcocer
  results: []
---

<!-- 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-model-jose-alcocer

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 beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0217
- Accuracy: 0.9925

## Model description

This model classifies beans between healthy, rust and angular_leaf_spot

## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1179        | 3.85  | 500  | 0.0217          | 0.9925   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3