---
license: apache-2.0
base_model: microsoft/beit-large-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_10x_beit_large_adamax_00001_fold5
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9183333333333333
---

<!-- 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. -->

# smids_10x_beit_large_adamax_00001_fold5

This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8705
- Accuracy: 0.9183

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.151         | 1.0   | 750   | 0.2341          | 0.9117   |
| 0.085         | 2.0   | 1500  | 0.2729          | 0.9117   |
| 0.0389        | 3.0   | 2250  | 0.3555          | 0.9183   |
| 0.0354        | 4.0   | 3000  | 0.4728          | 0.92     |
| 0.0161        | 5.0   | 3750  | 0.5494          | 0.9117   |
| 0.0006        | 6.0   | 4500  | 0.5920          | 0.9167   |
| 0.0191        | 7.0   | 5250  | 0.7177          | 0.9083   |
| 0.0025        | 8.0   | 6000  | 0.7193          | 0.9183   |
| 0.0296        | 9.0   | 6750  | 0.7219          | 0.9183   |
| 0.0071        | 10.0  | 7500  | 0.7346          | 0.9067   |
| 0.0001        | 11.0  | 8250  | 0.8516          | 0.9133   |
| 0.0012        | 12.0  | 9000  | 0.7790          | 0.9217   |
| 0.0009        | 13.0  | 9750  | 0.7769          | 0.9117   |
| 0.0           | 14.0  | 10500 | 0.8050          | 0.92     |
| 0.0           | 15.0  | 11250 | 0.7869          | 0.9167   |
| 0.0001        | 16.0  | 12000 | 0.8102          | 0.9133   |
| 0.0588        | 17.0  | 12750 | 0.7913          | 0.9183   |
| 0.0           | 18.0  | 13500 | 0.9080          | 0.9117   |
| 0.0           | 19.0  | 14250 | 0.7883          | 0.915    |
| 0.0           | 20.0  | 15000 | 0.8588          | 0.9183   |
| 0.0001        | 21.0  | 15750 | 0.8772          | 0.9167   |
| 0.0001        | 22.0  | 16500 | 0.8747          | 0.9133   |
| 0.0001        | 23.0  | 17250 | 0.7911          | 0.9217   |
| 0.0           | 24.0  | 18000 | 0.7828          | 0.9217   |
| 0.0           | 25.0  | 18750 | 0.7802          | 0.9233   |
| 0.0           | 26.0  | 19500 | 0.8237          | 0.92     |
| 0.0           | 27.0  | 20250 | 0.8003          | 0.9217   |
| 0.0           | 28.0  | 21000 | 0.8936          | 0.9133   |
| 0.0009        | 29.0  | 21750 | 0.8831          | 0.915    |
| 0.0181        | 30.0  | 22500 | 0.8036          | 0.9217   |
| 0.0           | 31.0  | 23250 | 0.7557          | 0.9267   |
| 0.0           | 32.0  | 24000 | 0.8859          | 0.92     |
| 0.0           | 33.0  | 24750 | 0.8754          | 0.92     |
| 0.0001        | 34.0  | 25500 | 0.8554          | 0.9117   |
| 0.0           | 35.0  | 26250 | 0.8615          | 0.9167   |
| 0.0           | 36.0  | 27000 | 0.8299          | 0.9217   |
| 0.0035        | 37.0  | 27750 | 0.8816          | 0.9167   |
| 0.0           | 38.0  | 28500 | 0.8681          | 0.9233   |
| 0.0           | 39.0  | 29250 | 0.8281          | 0.92     |
| 0.0           | 40.0  | 30000 | 0.8247          | 0.9183   |
| 0.0008        | 41.0  | 30750 | 0.8595          | 0.9183   |
| 0.0           | 42.0  | 31500 | 0.8563          | 0.92     |
| 0.0038        | 43.0  | 32250 | 0.8322          | 0.925    |
| 0.0           | 44.0  | 33000 | 0.8334          | 0.9183   |
| 0.0           | 45.0  | 33750 | 0.8475          | 0.9183   |
| 0.0           | 46.0  | 34500 | 0.8657          | 0.92     |
| 0.0           | 47.0  | 35250 | 0.8614          | 0.9183   |
| 0.0           | 48.0  | 36000 | 0.8662          | 0.92     |
| 0.0           | 49.0  | 36750 | 0.8708          | 0.9183   |
| 0.0           | 50.0  | 37500 | 0.8705          | 0.9183   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2