---
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_fold2
  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: 0.9151414309484193
---

<!-- 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_fold2

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: 0.9196
- Accuracy: 0.9151

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1587        | 1.0   | 750   | 0.2691          | 0.9101   |
| 0.0471        | 2.0   | 1500  | 0.3138          | 0.9135   |
| 0.0407        | 3.0   | 2250  | 0.4729          | 0.9118   |
| 0.0287        | 4.0   | 3000  | 0.5798          | 0.9068   |
| 0.012         | 5.0   | 3750  | 0.7233          | 0.9118   |
| 0.0109        | 6.0   | 4500  | 0.7175          | 0.9168   |
| 0.0017        | 7.0   | 5250  | 0.7940          | 0.9085   |
| 0.0129        | 8.0   | 6000  | 0.7917          | 0.9068   |
| 0.0001        | 9.0   | 6750  | 0.8466          | 0.9068   |
| 0.0033        | 10.0  | 7500  | 0.8662          | 0.9002   |
| 0.0001        | 11.0  | 8250  | 0.9262          | 0.9035   |
| 0.0005        | 12.0  | 9000  | 0.8648          | 0.9035   |
| 0.0001        | 13.0  | 9750  | 0.9176          | 0.9101   |
| 0.0001        | 14.0  | 10500 | 0.9531          | 0.8985   |
| 0.0002        | 15.0  | 11250 | 0.9250          | 0.9035   |
| 0.0418        | 16.0  | 12000 | 0.9389          | 0.9085   |
| 0.0           | 17.0  | 12750 | 0.9725          | 0.9035   |
| 0.0001        | 18.0  | 13500 | 0.9072          | 0.9101   |
| 0.0173        | 19.0  | 14250 | 0.9123          | 0.9151   |
| 0.0042        | 20.0  | 15000 | 0.9275          | 0.9068   |
| 0.0           | 21.0  | 15750 | 0.9111          | 0.9101   |
| 0.0243        | 22.0  | 16500 | 0.9348          | 0.9101   |
| 0.0002        | 23.0  | 17250 | 1.0125          | 0.9052   |
| 0.0002        | 24.0  | 18000 | 0.8943          | 0.9101   |
| 0.0           | 25.0  | 18750 | 1.0215          | 0.9035   |
| 0.0001        | 26.0  | 19500 | 0.9907          | 0.9085   |
| 0.0358        | 27.0  | 20250 | 0.9413          | 0.9101   |
| 0.0003        | 28.0  | 21000 | 0.8860          | 0.9201   |
| 0.0           | 29.0  | 21750 | 0.9273          | 0.9218   |
| 0.0           | 30.0  | 22500 | 0.9583          | 0.9068   |
| 0.0           | 31.0  | 23250 | 0.9280          | 0.9218   |
| 0.0           | 32.0  | 24000 | 0.9420          | 0.9168   |
| 0.0           | 33.0  | 24750 | 0.9244          | 0.9185   |
| 0.0           | 34.0  | 25500 | 0.9598          | 0.9085   |
| 0.0           | 35.0  | 26250 | 0.9576          | 0.9101   |
| 0.0           | 36.0  | 27000 | 0.9574          | 0.9101   |
| 0.0013        | 37.0  | 27750 | 0.9671          | 0.9101   |
| 0.0           | 38.0  | 28500 | 0.9627          | 0.9101   |
| 0.0           | 39.0  | 29250 | 0.9639          | 0.9118   |
| 0.0001        | 40.0  | 30000 | 0.9418          | 0.9118   |
| 0.0003        | 41.0  | 30750 | 0.9216          | 0.9135   |
| 0.0           | 42.0  | 31500 | 0.9226          | 0.9185   |
| 0.0           | 43.0  | 32250 | 0.9076          | 0.9218   |
| 0.0           | 44.0  | 33000 | 0.9133          | 0.9151   |
| 0.0006        | 45.0  | 33750 | 0.9164          | 0.9151   |
| 0.0           | 46.0  | 34500 | 0.9118          | 0.9168   |
| 0.0           | 47.0  | 35250 | 0.9173          | 0.9151   |
| 0.0           | 48.0  | 36000 | 0.9178          | 0.9101   |
| 0.0           | 49.0  | 36750 | 0.9196          | 0.9135   |
| 0.0           | 50.0  | 37500 | 0.9196          | 0.9151   |


### Framework versions

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