---
license: mit
base_model: shi-labs/nat-mini-in1k-224
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
- f1
model-index:
- name: nat-mini-in1k-224-finetuned-breakhis
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: image_folder
      type: image_folder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9669421487603306
    - name: F1
      type: f1
      value: 0.9612429172231991
---

<!-- 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. -->

# nat-mini-in1k-224-finetuned-breakhis

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 image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0983
- Accuracy: 0.9669
- F1: 0.9612
- Roc Auc: 0.9648

## 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-05
- 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:-------:|
| 0.3247        | 0.99  | 59   | 0.2084          | 0.9157   | 0.8968 | 0.8836  |
| 0.1338        | 2.0   | 119  | 0.1686          | 0.9355   | 0.9266 | 0.9437  |
| 0.1078        | 2.99  | 178  | 0.0986          | 0.9694   | 0.9636 | 0.9597  |
| 0.0795        | 4.0   | 238  | 0.0957          | 0.9719   | 0.9668 | 0.9660  |
| 0.0522        | 4.96  | 295  | 0.0983          | 0.9669   | 0.9612 | 0.9648  |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2