---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: leaves
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: defect
      type: imagefolder
      config: Dhika--Leaves
      split: validation
      args: Dhika--Leaves
    metrics:
    - name: Accuracy
      type: accuracy
      value: 1.0
---

<!-- 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. -->

# leaves

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 defect dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0012
- Accuracy: 1.0

## 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: 0.0002
- train_batch_size: 10
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2249        | 1.25  | 10   | 0.0323          | 1.0      |
| 0.0177        | 2.5   | 20   | 0.0112          | 1.0      |
| 0.0086        | 3.75  | 30   | 0.0075          | 1.0      |
| 0.0063        | 5.0   | 40   | 0.0059          | 1.0      |
| 0.0051        | 6.25  | 50   | 0.0050          | 1.0      |
| 0.0045        | 7.5   | 60   | 0.0044          | 1.0      |
| 0.004         | 8.75  | 70   | 0.0040          | 1.0      |
| 0.0036        | 10.0  | 80   | 0.0036          | 1.0      |
| 0.0033        | 11.25 | 90   | 0.0034          | 1.0      |
| 0.0031        | 12.5  | 100  | 0.0031          | 1.0      |
| 0.0028        | 13.75 | 110  | 0.0029          | 1.0      |
| 0.0026        | 15.0  | 120  | 0.0027          | 1.0      |
| 0.0025        | 16.25 | 130  | 0.0025          | 1.0      |
| 0.0023        | 17.5  | 140  | 0.0024          | 1.0      |
| 0.0022        | 18.75 | 150  | 0.0023          | 1.0      |
| 0.0021        | 20.0  | 160  | 0.0021          | 1.0      |
| 0.002         | 21.25 | 170  | 0.0020          | 1.0      |
| 0.0019        | 22.5  | 180  | 0.0019          | 1.0      |
| 0.0018        | 23.75 | 190  | 0.0019          | 1.0      |
| 0.0017        | 25.0  | 200  | 0.0018          | 1.0      |
| 0.0016        | 26.25 | 210  | 0.0017          | 1.0      |
| 0.0016        | 27.5  | 220  | 0.0017          | 1.0      |
| 0.0015        | 28.75 | 230  | 0.0016          | 1.0      |
| 0.0015        | 30.0  | 240  | 0.0015          | 1.0      |
| 0.0014        | 31.25 | 250  | 0.0015          | 1.0      |
| 0.0014        | 32.5  | 260  | 0.0015          | 1.0      |
| 0.0013        | 33.75 | 270  | 0.0014          | 1.0      |
| 0.0013        | 35.0  | 280  | 0.0014          | 1.0      |
| 0.0013        | 36.25 | 290  | 0.0014          | 1.0      |
| 0.0013        | 37.5  | 300  | 0.0013          | 1.0      |
| 0.0012        | 38.75 | 310  | 0.0013          | 1.0      |
| 0.0012        | 40.0  | 320  | 0.0013          | 1.0      |
| 0.0012        | 41.25 | 330  | 0.0013          | 1.0      |
| 0.0012        | 42.5  | 340  | 0.0013          | 1.0      |
| 0.0012        | 43.75 | 350  | 0.0012          | 1.0      |
| 0.0012        | 45.0  | 360  | 0.0012          | 1.0      |
| 0.0011        | 46.25 | 370  | 0.0012          | 1.0      |
| 0.0012        | 47.5  | 380  | 0.0012          | 1.0      |
| 0.0011        | 48.75 | 390  | 0.0012          | 1.0      |
| 0.0011        | 50.0  | 400  | 0.0012          | 1.0      |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3