---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
model-index:
- name: Rice-Plant-50-Epochs-Model
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9688473520249221
    - name: F1
      type: f1
      value: 0.9686087085518211
---

<!-- 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. -->

# Rice-Plant-50-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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1649
- Accuracy: 0.9688
- F1: 0.9686

## 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0399        | 1.0   | 115  | 0.6185          | 0.8910   | 0.8933 |
| 0.3392        | 2.0   | 230  | 0.2849          | 0.9502   | 0.9497 |
| 0.1633        | 3.0   | 345  | 0.2230          | 0.9439   | 0.9440 |
| 0.104         | 4.0   | 460  | 0.2022          | 0.9502   | 0.9495 |
| 0.0828        | 5.0   | 575  | 0.2081          | 0.9408   | 0.9406 |
| 0.0603        | 6.0   | 690  | 0.2301          | 0.9408   | 0.9403 |
| 0.0513        | 7.0   | 805  | 0.1704          | 0.9595   | 0.9593 |
| 0.042         | 8.0   | 920  | 0.1587          | 0.9626   | 0.9626 |
| 0.0356        | 9.0   | 1035 | 0.1606          | 0.9626   | 0.9625 |
| 0.0299        | 10.0  | 1150 | 0.1608          | 0.9657   | 0.9656 |
| 0.0262        | 11.0  | 1265 | 0.1553          | 0.9626   | 0.9625 |
| 0.0232        | 12.0  | 1380 | 0.1582          | 0.9657   | 0.9656 |
| 0.0207        | 13.0  | 1495 | 0.1588          | 0.9657   | 0.9656 |
| 0.0186        | 14.0  | 1610 | 0.1618          | 0.9657   | 0.9656 |
| 0.0168        | 15.0  | 1725 | 0.1618          | 0.9657   | 0.9656 |
| 0.0152        | 16.0  | 1840 | 0.1639          | 0.9657   | 0.9656 |
| 0.0139        | 17.0  | 1955 | 0.1649          | 0.9688   | 0.9686 |
| 0.0127        | 18.0  | 2070 | 0.1676          | 0.9657   | 0.9656 |
| 0.0117        | 19.0  | 2185 | 0.1688          | 0.9688   | 0.9686 |
| 0.0108        | 20.0  | 2300 | 0.1710          | 0.9626   | 0.9622 |
| 0.01          | 21.0  | 2415 | 0.1723          | 0.9657   | 0.9654 |
| 0.0093        | 22.0  | 2530 | 0.1739          | 0.9657   | 0.9654 |
| 0.0087        | 23.0  | 2645 | 0.1758          | 0.9626   | 0.9622 |
| 0.0081        | 24.0  | 2760 | 0.1776          | 0.9626   | 0.9622 |
| 0.0076        | 25.0  | 2875 | 0.1777          | 0.9657   | 0.9654 |
| 0.0071        | 26.0  | 2990 | 0.1792          | 0.9657   | 0.9654 |
| 0.0067        | 27.0  | 3105 | 0.1808          | 0.9657   | 0.9654 |
| 0.0063        | 28.0  | 3220 | 0.1822          | 0.9657   | 0.9654 |
| 0.006         | 29.0  | 3335 | 0.1834          | 0.9657   | 0.9654 |
| 0.0057        | 30.0  | 3450 | 0.1840          | 0.9657   | 0.9654 |
| 0.0054        | 31.0  | 3565 | 0.1855          | 0.9657   | 0.9654 |
| 0.0051        | 32.0  | 3680 | 0.1868          | 0.9657   | 0.9654 |
| 0.0049        | 33.0  | 3795 | 0.1877          | 0.9657   | 0.9654 |
| 0.0047        | 34.0  | 3910 | 0.1892          | 0.9657   | 0.9654 |
| 0.0045        | 35.0  | 4025 | 0.1900          | 0.9657   | 0.9654 |
| 0.0043        | 36.0  | 4140 | 0.1914          | 0.9657   | 0.9654 |
| 0.0042        | 37.0  | 4255 | 0.1919          | 0.9657   | 0.9654 |
| 0.004         | 38.0  | 4370 | 0.1929          | 0.9657   | 0.9654 |
| 0.0039        | 39.0  | 4485 | 0.1938          | 0.9657   | 0.9654 |
| 0.0037        | 40.0  | 4600 | 0.1953          | 0.9657   | 0.9654 |
| 0.0036        | 41.0  | 4715 | 0.1956          | 0.9657   | 0.9654 |
| 0.0035        | 42.0  | 4830 | 0.1965          | 0.9657   | 0.9654 |
| 0.0035        | 43.0  | 4945 | 0.1974          | 0.9657   | 0.9654 |
| 0.0034        | 44.0  | 5060 | 0.1981          | 0.9657   | 0.9654 |
| 0.0033        | 45.0  | 5175 | 0.1984          | 0.9657   | 0.9654 |
| 0.0032        | 46.0  | 5290 | 0.1986          | 0.9657   | 0.9654 |
| 0.0032        | 47.0  | 5405 | 0.1989          | 0.9657   | 0.9654 |
| 0.0032        | 48.0  | 5520 | 0.1993          | 0.9657   | 0.9654 |
| 0.0031        | 49.0  | 5635 | 0.1993          | 0.9657   | 0.9654 |
| 0.0031        | 50.0  | 5750 | 0.1993          | 0.9657   | 0.9654 |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1