---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: msi-dinat-mini
  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: 0.6307660050321499
    - name: F1
      type: f1
      value: 0.45316219853017287
    - name: Precision
      type: precision
      value: 0.6338497176777182
    - name: Recall
      type: recall
      value: 0.3526379379782521
---

<!-- 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-dinat-mini

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8735
- Accuracy: 0.6308
- F1: 0.4532
- Precision: 0.6338
- Recall: 0.3526

## 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: 1e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.5414        | 1.0   | 2015  | 0.7584          | 0.5874   | 0.3960 | 0.5427    | 0.3117 |
| 0.4715        | 2.0   | 4031  | 0.7695          | 0.6208   | 0.4593 | 0.6021    | 0.3712 |
| 0.4159        | 3.0   | 6047  | 0.7922          | 0.6230   | 0.4637 | 0.6056    | 0.3757 |
| 0.3774        | 4.0   | 8063  | 0.8166          | 0.6286   | 0.4589 | 0.6235    | 0.3630 |
| 0.3635        | 5.0   | 10078 | 0.8123          | 0.6349   | 0.4889 | 0.6225    | 0.4026 |
| 0.3471        | 6.0   | 12094 | 0.8481          | 0.6265   | 0.4575 | 0.6186    | 0.3630 |
| 0.3616        | 7.0   | 14110 | 0.8605          | 0.6284   | 0.4514 | 0.6279    | 0.3524 |
| 0.3517        | 8.0   | 16126 | 0.8661          | 0.6329   | 0.4600 | 0.6356    | 0.3604 |
| 0.3476        | 9.0   | 18141 | 0.8631          | 0.6330   | 0.4619 | 0.6346    | 0.3631 |
| 0.3469        | 10.0  | 20150 | 0.8735          | 0.6308   | 0.4532 | 0.6338    | 0.3526 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0