---
license: apache-2.0
base_model: WinKawaks/vit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: msi-vit-small-1218-2
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6164383561643836
    - name: F1
      type: f1
      value: 0.3276157804459692
    - name: Precision
      type: precision
      value: 0.6840624200562804
    - name: Recall
      type: recall
      value: 0.2153846153846154
---

<!-- 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. -->

# msi-vit-small-1218-2

This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co./WinKawaks/vit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3372
- Accuracy: 0.6164
- F1: 0.3276
- Precision: 0.6841
- Recall: 0.2154

## 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-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4367        | 1.0   | 1008  | 0.6603          | 0.6572   | 0.5313 | 0.6530    | 0.4478 |
| 0.2161        | 2.0   | 2016  | 0.8021          | 0.6329   | 0.4989 | 0.6118    | 0.4211 |
| 0.169         | 3.0   | 3024  | 1.4062          | 0.6010   | 0.2653 | 0.6592    | 0.1661 |
| 0.1543        | 4.0   | 4032  | 1.1498          | 0.6259   | 0.3670 | 0.6903    | 0.2499 |
| 0.1534        | 5.0   | 5040  | 1.5067          | 0.6208   | 0.3519 | 0.6808    | 0.2373 |
| 0.1596        | 6.0   | 6048  | 0.8837          | 0.6504   | 0.6505 | 0.5744    | 0.7498 |
| 0.1504        | 7.0   | 7056  | 1.0030          | 0.6302   | 0.4192 | 0.6580    | 0.3075 |
| 0.1795        | 8.0   | 8064  | 1.3908          | 0.5953   | 0.2950 | 0.6041    | 0.1952 |
| 0.1636        | 9.0   | 9072  | 1.1040          | 0.6290   | 0.4619 | 0.6230    | 0.3671 |
| 0.1629        | 10.0  | 10080 | 1.3372          | 0.6164   | 0.3276 | 0.6841    | 0.2154 |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0