---
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-fold1
  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: 0.6944444444444444
---

<!-- 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-fold1

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: 0.8281
- Accuracy: 0.6944

## 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    | 1.1951          | 0.3333   |
| No log        | 1.7143  | 3    | 1.2409          | 0.3056   |
| No log        | 2.8571  | 5    | 1.2181          | 0.3056   |
| No log        | 4.0     | 7    | 1.1308          | 0.5      |
| No log        | 4.5714  | 8    | 1.1858          | 0.5278   |
| 1.092         | 5.7143  | 10   | 1.2798          | 0.3056   |
| 1.092         | 6.8571  | 12   | 1.0892          | 0.3611   |
| 1.092         | 8.0     | 14   | 1.2658          | 0.3889   |
| 1.092         | 8.5714  | 15   | 1.1603          | 0.5      |
| 1.092         | 9.7143  | 17   | 1.0786          | 0.4167   |
| 1.092         | 10.8571 | 19   | 1.2400          | 0.4167   |
| 0.9727        | 12.0    | 21   | 1.2929          | 0.3889   |
| 0.9727        | 12.5714 | 22   | 1.1613          | 0.3889   |
| 0.9727        | 13.7143 | 24   | 0.9838          | 0.6111   |
| 0.9727        | 14.8571 | 26   | 1.2316          | 0.3889   |
| 0.9727        | 16.0    | 28   | 1.1520          | 0.4722   |
| 0.9727        | 16.5714 | 29   | 1.0345          | 0.5556   |
| 0.8844        | 17.7143 | 31   | 1.0000          | 0.5      |
| 0.8844        | 18.8571 | 33   | 0.9933          | 0.5278   |
| 0.8844        | 20.0    | 35   | 1.0512          | 0.5556   |
| 0.8844        | 20.5714 | 36   | 0.9950          | 0.5556   |
| 0.8844        | 21.7143 | 38   | 0.9621          | 0.4722   |
| 0.7447        | 22.8571 | 40   | 0.8812          | 0.5278   |
| 0.7447        | 24.0    | 42   | 1.0244          | 0.5833   |
| 0.7447        | 24.5714 | 43   | 1.0124          | 0.5833   |
| 0.7447        | 25.7143 | 45   | 0.8908          | 0.6389   |
| 0.7447        | 26.8571 | 47   | 0.8185          | 0.5833   |
| 0.7447        | 28.0    | 49   | 0.9409          | 0.5556   |
| 0.6176        | 28.5714 | 50   | 1.0401          | 0.5556   |
| 0.6176        | 29.7143 | 52   | 1.0989          | 0.5556   |
| 0.6176        | 30.8571 | 54   | 0.9102          | 0.5833   |
| 0.6176        | 32.0    | 56   | 0.8855          | 0.5833   |
| 0.6176        | 32.5714 | 57   | 0.8974          | 0.5556   |
| 0.6176        | 33.7143 | 59   | 0.9419          | 0.6111   |
| 0.4929        | 34.8571 | 61   | 0.9471          | 0.5833   |
| 0.4929        | 36.0    | 63   | 0.8609          | 0.5833   |
| 0.4929        | 36.5714 | 64   | 0.8558          | 0.5833   |
| 0.4929        | 37.7143 | 66   | 0.8449          | 0.5556   |
| 0.4929        | 38.8571 | 68   | 0.8136          | 0.6667   |
| 0.463         | 40.0    | 70   | 0.8281          | 0.6944   |
| 0.463         | 40.5714 | 71   | 0.8227          | 0.6944   |
| 0.463         | 41.7143 | 73   | 0.8323          | 0.5833   |
| 0.463         | 42.8571 | 75   | 0.8436          | 0.5833   |
| 0.463         | 44.0    | 77   | 0.8390          | 0.5833   |
| 0.463         | 44.5714 | 78   | 0.8580          | 0.6111   |
| 0.3995        | 45.7143 | 80   | 0.9375          | 0.6111   |
| 0.3995        | 46.8571 | 82   | 0.9897          | 0.5556   |
| 0.3995        | 48.0    | 84   | 0.9785          | 0.5556   |
| 0.3995        | 48.5714 | 85   | 0.9336          | 0.6389   |
| 0.3995        | 49.7143 | 87   | 0.8504          | 0.6389   |
| 0.3995        | 50.8571 | 89   | 0.8450          | 0.6667   |
| 0.3697        | 52.0    | 91   | 0.8531          | 0.6389   |
| 0.3697        | 52.5714 | 92   | 0.8728          | 0.6389   |
| 0.3697        | 53.7143 | 94   | 0.9076          | 0.6667   |
| 0.3697        | 54.8571 | 96   | 0.9175          | 0.6389   |
| 0.3697        | 56.0    | 98   | 0.9145          | 0.5833   |
| 0.3697        | 56.5714 | 99   | 0.9119          | 0.5833   |
| 0.3259        | 57.1429 | 100  | 0.9102          | 0.5833   |


### Framework versions

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