transformers pytorch beit image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/beit-base-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_sgd_00001_fold4
  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: 0.3883333333333333
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# smids_5x_beit_base_sgd_00001_fold4

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: 1.1429
- Accuracy: 0.3883

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2481        | 1.0   | 375   | 1.3387          | 0.3367   |
| 1.2853        | 2.0   | 750   | 1.3297          | 0.34     |
| 1.2245        | 3.0   | 1125  | 1.3209          | 0.34     |
| 1.2269        | 4.0   | 1500  | 1.3124          | 0.34     |
| 1.1881        | 5.0   | 1875  | 1.3044          | 0.34     |
| 1.1962        | 6.0   | 2250  | 1.2965          | 0.3417   |
| 1.1766        | 7.0   | 2625  | 1.2890          | 0.345    |
| 1.1422        | 8.0   | 3000  | 1.2816          | 0.3467   |
| 1.1265        | 9.0   | 3375  | 1.2745          | 0.3483   |
| 1.1532        | 10.0  | 3750  | 1.2676          | 0.3517   |
| 1.1456        | 11.0  | 4125  | 1.2609          | 0.3533   |
| 1.1221        | 12.0  | 4500  | 1.2545          | 0.355    |
| 1.1397        | 13.0  | 4875  | 1.2483          | 0.355    |
| 1.1323        | 14.0  | 5250  | 1.2422          | 0.3583   |
| 1.1113        | 15.0  | 5625  | 1.2362          | 0.3617   |
| 1.1197        | 16.0  | 6000  | 1.2307          | 0.3633   |
| 1.1175        | 17.0  | 6375  | 1.2251          | 0.3667   |
| 1.1137        | 18.0  | 6750  | 1.2199          | 0.3683   |
| 1.1317        | 19.0  | 7125  | 1.2149          | 0.3667   |
| 1.0985        | 20.0  | 7500  | 1.2099          | 0.37     |
| 1.1037        | 21.0  | 7875  | 1.2054          | 0.37     |
| 1.1051        | 22.0  | 8250  | 1.2008          | 0.3717   |
| 1.1012        | 23.0  | 8625  | 1.1965          | 0.3717   |
| 1.0418        | 24.0  | 9000  | 1.1925          | 0.375    |
| 1.0922        | 25.0  | 9375  | 1.1886          | 0.3767   |
| 1.0809        | 26.0  | 9750  | 1.1848          | 0.3767   |
| 1.096         | 27.0  | 10125 | 1.1812          | 0.3767   |
| 1.0328        | 28.0  | 10500 | 1.1778          | 0.375    |
| 1.0501        | 29.0  | 10875 | 1.1745          | 0.3767   |
| 1.065         | 30.0  | 11250 | 1.1714          | 0.3767   |
| 1.0717        | 31.0  | 11625 | 1.1685          | 0.3783   |
| 1.104         | 32.0  | 12000 | 1.1658          | 0.3767   |
| 1.0567        | 33.0  | 12375 | 1.1632          | 0.3783   |
| 1.0632        | 34.0  | 12750 | 1.1607          | 0.3783   |
| 1.0635        | 35.0  | 13125 | 1.1585          | 0.3783   |
| 1.0477        | 36.0  | 13500 | 1.1563          | 0.3833   |
| 1.0721        | 37.0  | 13875 | 1.1544          | 0.385    |
| 1.0594        | 38.0  | 14250 | 1.1526          | 0.385    |
| 1.0484        | 39.0  | 14625 | 1.1510          | 0.3867   |
| 1.0408        | 40.0  | 15000 | 1.1495          | 0.3883   |
| 1.0421        | 41.0  | 15375 | 1.1482          | 0.39     |
| 1.0561        | 42.0  | 15750 | 1.1470          | 0.3883   |
| 1.0338        | 43.0  | 16125 | 1.1460          | 0.3883   |
| 1.0224        | 44.0  | 16500 | 1.1451          | 0.3883   |
| 1.0269        | 45.0  | 16875 | 1.1444          | 0.3883   |
| 1.0608        | 46.0  | 17250 | 1.1438          | 0.3883   |
| 1.0652        | 47.0  | 17625 | 1.1434          | 0.3883   |
| 1.0189        | 48.0  | 18000 | 1.1431          | 0.3883   |
| 1.0225        | 49.0  | 18375 | 1.1429          | 0.3883   |
| 1.0356        | 50.0  | 18750 | 1.1429          | 0.3883   |


### Framework versions

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