---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
- f1
model-index:
- name: Corn-Plant-1-Epochs-Model
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: image_folder
      type: image_folder
      config: data
      split: train
      args: data
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8433734939759037
    - name: F1
      type: f1
      value: 0.8932628786809459
---

<!-- 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. -->

# Corn-Plant-1-Epochs-Model

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: 0.4730
- Accuracy: 0.8434
- F1: 0.8933

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.646         | 1.0   | 94   | 0.4730          | 0.8434   | 0.8933 |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1