---
license: mit
base_model: shi-labs/nat-mini-in1k-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: msi_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: 0.6228683254123567
---

<!-- 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_mini

This model is a fine-tuned version of [shi-labs/nat-mini-in1k-224](https://huggingface.co./shi-labs/nat-mini-in1k-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5314
- Accuracy: 0.6229

## 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-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.428         | 1.0   | 2015  | 0.8665          | 0.6079   |
| 0.3163        | 2.0   | 4031  | 1.0921          | 0.6169   |
| 0.2805        | 3.0   | 6047  | 1.1998          | 0.6082   |
| 0.2251        | 4.0   | 8063  | 1.2788          | 0.6126   |
| 0.1988        | 5.0   | 10078 | 1.3336          | 0.6121   |
| 0.1794        | 6.0   | 12094 | 1.3361          | 0.6224   |
| 0.1724        | 7.0   | 14110 | 1.5478          | 0.6097   |
| 0.1739        | 8.0   | 16126 | 1.6165          | 0.6169   |
| 0.1637        | 9.0   | 18141 | 1.5974          | 0.6134   |
| 0.1667        | 10.0  | 20150 | 1.5314          | 0.6229   |


### Framework versions

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