---
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: All-Plants-18-Epochs-Model
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: image_folder
      type: image_folder
      config: Dataset
      split: train
      args: Dataset
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9847645429362881
    - name: F1
      type: f1
      value: 0.984922643975302
---

<!-- 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. -->

# All-Plants-18-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: 0.0888
- Accuracy: 0.9848
- F1: 0.9849

## 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: 18

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.9212        | 1.0   | 407  | 0.3931          | 0.9501   | 0.9579 |
| 0.2659        | 2.0   | 814  | 0.2176          | 0.9668   | 0.9674 |
| 0.137         | 3.0   | 1221 | 0.1481          | 0.9723   | 0.9731 |
| 0.0865        | 4.0   | 1628 | 0.1043          | 0.9834   | 0.9836 |
| 0.0557        | 5.0   | 2035 | 0.0888          | 0.9848   | 0.9849 |
| 0.0408        | 6.0   | 2442 | 0.0839          | 0.9848   | 0.9848 |
| 0.0289        | 7.0   | 2849 | 0.0920          | 0.9848   | 0.9849 |
| 0.0229        | 8.0   | 3256 | 0.0817          | 0.9834   | 0.9837 |
| 0.0175        | 9.0   | 3663 | 0.0890          | 0.9820   | 0.9823 |
| 0.0156        | 10.0  | 4070 | 0.0966          | 0.9820   | 0.9823 |
| 0.0121        | 11.0  | 4477 | 0.0809          | 0.9834   | 0.9837 |
| 0.0102        | 12.0  | 4884 | 0.0875          | 0.9820   | 0.9823 |
| 0.0086        | 13.0  | 5291 | 0.0873          | 0.9820   | 0.9823 |
| 0.0077        | 14.0  | 5698 | 0.0860          | 0.9820   | 0.9823 |
| 0.0068        | 15.0  | 6105 | 0.0876          | 0.9820   | 0.9823 |
| 0.0062        | 16.0  | 6512 | 0.0896          | 0.9820   | 0.9823 |
| 0.0059        | 17.0  | 6919 | 0.0890          | 0.9820   | 0.9823 |
| 0.0056        | 18.0  | 7326 | 0.0894          | 0.9820   | 0.9823 |


### Framework versions

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