---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-hasta-65-fold5
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5555555555555556
---

<!-- 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. -->

# beit-base-patch16-224-hasta-65-fold5

This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1241
- Accuracy: 0.5556

## 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: 100

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.5714  | 1    | 1.1680          | 0.3333   |
| No log        | 1.7143  | 3    | 1.2100          | 0.1944   |
| No log        | 2.8571  | 5    | 1.3667          | 0.2778   |
| No log        | 4.0     | 7    | 1.1208          | 0.3889   |
| No log        | 4.5714  | 8    | 1.1168          | 0.3611   |
| 1.132         | 5.7143  | 10   | 1.4031          | 0.2778   |
| 1.132         | 6.8571  | 12   | 1.2012          | 0.3333   |
| 1.132         | 8.0     | 14   | 1.2353          | 0.2778   |
| 1.132         | 8.5714  | 15   | 1.2099          | 0.3056   |
| 1.132         | 9.7143  | 17   | 1.0942          | 0.3611   |
| 1.132         | 10.8571 | 19   | 1.1301          | 0.4444   |
| 1.0271        | 12.0    | 21   | 1.0591          | 0.4167   |
| 1.0271        | 12.5714 | 22   | 1.0648          | 0.4444   |
| 1.0271        | 13.7143 | 24   | 1.1125          | 0.4722   |
| 1.0271        | 14.8571 | 26   | 1.1097          | 0.4722   |
| 1.0271        | 16.0    | 28   | 1.0616          | 0.4444   |
| 1.0271        | 16.5714 | 29   | 1.0284          | 0.4722   |
| 0.9507        | 17.7143 | 31   | 1.0291          | 0.5      |
| 0.9507        | 18.8571 | 33   | 1.0692          | 0.4722   |
| 0.9507        | 20.0    | 35   | 1.1153          | 0.5      |
| 0.9507        | 20.5714 | 36   | 1.1719          | 0.4444   |
| 0.9507        | 21.7143 | 38   | 1.0161          | 0.4444   |
| 0.8001        | 22.8571 | 40   | 1.1361          | 0.4444   |
| 0.8001        | 24.0    | 42   | 1.3277          | 0.4444   |
| 0.8001        | 24.5714 | 43   | 1.1331          | 0.5      |
| 0.8001        | 25.7143 | 45   | 1.0659          | 0.4722   |
| 0.8001        | 26.8571 | 47   | 1.1309          | 0.5278   |
| 0.8001        | 28.0    | 49   | 1.1241          | 0.5556   |
| 0.7175        | 28.5714 | 50   | 1.1371          | 0.5278   |
| 0.7175        | 29.7143 | 52   | 1.0928          | 0.5      |
| 0.7175        | 30.8571 | 54   | 1.2129          | 0.4444   |
| 0.7175        | 32.0    | 56   | 1.0321          | 0.5      |
| 0.7175        | 32.5714 | 57   | 1.0809          | 0.5278   |
| 0.7175        | 33.7143 | 59   | 0.9813          | 0.5278   |
| 0.6766        | 34.8571 | 61   | 1.0617          | 0.5      |
| 0.6766        | 36.0    | 63   | 0.9618          | 0.5278   |
| 0.6766        | 36.5714 | 64   | 0.9541          | 0.5556   |
| 0.6766        | 37.7143 | 66   | 0.9689          | 0.5278   |
| 0.6766        | 38.8571 | 68   | 1.1063          | 0.5556   |
| 0.5934        | 40.0    | 70   | 1.0139          | 0.5      |
| 0.5934        | 40.5714 | 71   | 1.0087          | 0.5      |
| 0.5934        | 41.7143 | 73   | 1.0309          | 0.5      |
| 0.5934        | 42.8571 | 75   | 1.0636          | 0.5      |
| 0.5934        | 44.0    | 77   | 1.1057          | 0.5      |
| 0.5934        | 44.5714 | 78   | 1.1015          | 0.4722   |
| 0.4926        | 45.7143 | 80   | 1.0938          | 0.5278   |
| 0.4926        | 46.8571 | 82   | 1.0807          | 0.5      |
| 0.4926        | 48.0    | 84   | 1.1275          | 0.5278   |
| 0.4926        | 48.5714 | 85   | 1.1604          | 0.5278   |
| 0.4926        | 49.7143 | 87   | 1.1296          | 0.5278   |
| 0.4926        | 50.8571 | 89   | 1.0748          | 0.5278   |
| 0.4964        | 52.0    | 91   | 1.0716          | 0.5278   |
| 0.4964        | 52.5714 | 92   | 1.0780          | 0.5278   |
| 0.4964        | 53.7143 | 94   | 1.0755          | 0.5278   |
| 0.4964        | 54.8571 | 96   | 1.0680          | 0.5278   |
| 0.4964        | 56.0    | 98   | 1.0676          | 0.5278   |
| 0.4964        | 56.5714 | 99   | 1.0692          | 0.5278   |
| 0.404         | 57.1429 | 100  | 1.0692          | 0.5278   |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1