---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: msi-nat-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: 0.6308708414872799
    - name: F1
      type: f1
      value: 0.47632740072381147
    - name: Precision
      type: precision
      value: 0.6193914388860238
    - name: Recall
      type: recall
      value: 0.3869512686266613
---

<!-- 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-nat-mini

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8600
- Accuracy: 0.6309
- F1: 0.4763
- Precision: 0.6194
- Recall: 0.3870

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.5496        | 1.0   | 2015  | 0.7573          | 0.5955   | 0.4196 | 0.5559    | 0.3369 |
| 0.4807        | 2.0   | 4031  | 0.7416          | 0.6309   | 0.4981 | 0.6074    | 0.4222 |
| 0.4235        | 3.0   | 6047  | 0.7680          | 0.6325   | 0.5047 | 0.6076    | 0.4317 |
| 0.3879        | 4.0   | 8063  | 0.7875          | 0.6339   | 0.4923 | 0.6179    | 0.4092 |
| 0.3702        | 5.0   | 10078 | 0.7923          | 0.6383   | 0.5128 | 0.6168    | 0.4388 |
| 0.3568        | 6.0   | 12094 | 0.8311          | 0.6313   | 0.4969 | 0.6090    | 0.4197 |
| 0.3661        | 7.0   | 14110 | 0.8345          | 0.6316   | 0.4843 | 0.6166    | 0.3987 |
| 0.354         | 8.0   | 16126 | 0.8501          | 0.6305   | 0.4800 | 0.6162    | 0.3931 |
| 0.3569        | 9.0   | 18141 | 0.8552          | 0.6318   | 0.4809 | 0.6193    | 0.3931 |
| 0.3536        | 10.0  | 20150 | 0.8600          | 0.6309   | 0.4763 | 0.6194    | 0.3870 |


### Framework versions

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