---
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: 0.8460220784164446
    - name: F1
      type: f1
      value: 0.8017318846499469
    - name: Precision
      type: precision
      value: 0.8296559303406882
    - name: Recall
      type: recall
      value: 0.7756263336758081
---

<!-- 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: 0.3451
- Accuracy: 0.8460
- F1: 0.8017
- Precision: 0.8297
- Recall: 0.7756

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.5705        | 1.0   | 1970  | 0.5230          | 0.7410   | 0.6588 | 0.6988    | 0.6232 |
| 0.4805        | 2.0   | 3941  | 0.4447          | 0.7924   | 0.7298 | 0.7640    | 0.6986 |
| 0.4521        | 3.0   | 5911  | 0.4090          | 0.8107   | 0.7518 | 0.7936    | 0.7141 |
| 0.4343        | 4.0   | 7882  | 0.3878          | 0.8239   | 0.7768 | 0.7907    | 0.7634 |
| 0.4003        | 5.0   | 9852  | 0.3720          | 0.8328   | 0.7850 | 0.8113    | 0.7604 |
| 0.3887        | 6.0   | 11823 | 0.3620          | 0.8376   | 0.7875 | 0.8295    | 0.7496 |
| 0.3709        | 7.0   | 13793 | 0.3506          | 0.8435   | 0.7977 | 0.8286    | 0.7690 |
| 0.3686        | 8.0   | 15764 | 0.3473          | 0.8461   | 0.8025 | 0.8271    | 0.7793 |
| 0.3819        | 9.0   | 17734 | 0.3422          | 0.8476   | 0.8052 | 0.8270    | 0.7845 |
| 0.3838        | 10.0  | 19700 | 0.3451          | 0.8460   | 0.8017 | 0.8297    | 0.7756 |


### Framework versions

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