---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-Trial008-YEL_STEM
  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: 0.9411764705882353
---

<!-- 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-base-patch16-224-Trial008-YEL_STEM

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0958
- Accuracy: 0.9412

## 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: 5e-05
- train_batch_size: 60
- eval_batch_size: 60
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 240
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.544         | 1.0   | 1    | 0.8179          | 0.4118   |
| 0.3416        | 2.0   | 3    | 0.7448          | 0.5294   |
| 0.1412        | 3.0   | 5    | 0.7606          | 0.5294   |
| 0.4868        | 4.0   | 6    | 0.5647          | 0.6471   |
| 0.3852        | 5.0   | 7    | 0.4646          | 0.8235   |
| 0.284         | 6.0   | 9    | 0.4300          | 0.8235   |
| 0.1075        | 7.0   | 11   | 0.4628          | 0.8235   |
| 0.3243        | 8.0   | 12   | 0.4687          | 0.7647   |
| 0.3317        | 9.0   | 13   | 0.4089          | 0.8235   |
| 0.146         | 10.0  | 15   | 0.3330          | 0.8824   |
| 0.0762        | 11.0  | 17   | 0.2941          | 0.8824   |
| 0.2351        | 12.0  | 18   | 0.3217          | 0.8824   |
| 0.2458        | 13.0  | 19   | 0.3705          | 0.8824   |
| 0.1431        | 14.0  | 21   | 0.3138          | 0.8824   |
| 0.0883        | 15.0  | 23   | 0.1510          | 0.9412   |
| 0.1601        | 16.0  | 24   | 0.1373          | 0.9412   |
| 0.2212        | 17.0  | 25   | 0.1175          | 0.9412   |
| 0.1311        | 18.0  | 27   | 0.1130          | 0.9412   |
| 0.0801        | 19.0  | 29   | 0.1506          | 0.9412   |
| 0.1857        | 20.0  | 30   | 0.1272          | 0.9412   |
| 0.241         | 21.0  | 31   | 0.0974          | 0.9412   |
| 0.1098        | 22.0  | 33   | 0.0593          | 1.0      |
| 0.0464        | 23.0  | 35   | 0.0574          | 1.0      |
| 0.1757        | 24.0  | 36   | 0.0554          | 1.0      |
| 0.1992        | 25.0  | 37   | 0.0605          | 1.0      |
| 0.1167        | 26.0  | 39   | 0.0818          | 0.9412   |
| 0.0703        | 27.0  | 41   | 0.1177          | 0.9412   |
| 0.1382        | 28.0  | 42   | 0.1281          | 0.9412   |
| 0.1563        | 29.0  | 43   | 0.1357          | 0.9412   |
| 0.1113        | 30.0  | 45   | 0.1417          | 0.8824   |
| 0.0639        | 31.0  | 47   | 0.1250          | 0.9412   |
| 0.1564        | 32.0  | 48   | 0.1107          | 0.9412   |
| 0.1877        | 33.0  | 49   | 0.1002          | 0.9412   |
| 0.06          | 33.33 | 50   | 0.0958          | 0.9412   |


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 1.12.1
- Datasets 2.12.0
- Tokenizers 0.13.1