transformers tensorboard safetensors 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: hushem_1x_beit_base_rms_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: 0.8444444444444444
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# hushem_1x_beit_base_rms_00001_fold2

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: 0.9358
- Accuracy: 0.8444

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 6    | 1.2856          | 0.4889   |
| 1.4398        | 2.0   | 12   | 0.9696          | 0.6222   |
| 1.4398        | 3.0   | 18   | 0.7405          | 0.7111   |
| 0.463         | 4.0   | 24   | 0.8561          | 0.7333   |
| 0.1243        | 5.0   | 30   | 0.6572          | 0.8222   |
| 0.1243        | 6.0   | 36   | 0.6983          | 0.8444   |
| 0.0205        | 7.0   | 42   | 0.7294          | 0.8222   |
| 0.0205        | 8.0   | 48   | 0.6504          | 0.8      |
| 0.0064        | 9.0   | 54   | 0.6828          | 0.8222   |
| 0.0142        | 10.0  | 60   | 0.6539          | 0.8222   |
| 0.0142        | 11.0  | 66   | 0.7615          | 0.8444   |
| 0.0032        | 12.0  | 72   | 0.8146          | 0.8444   |
| 0.0032        | 13.0  | 78   | 0.8154          | 0.8444   |
| 0.0019        | 14.0  | 84   | 0.7947          | 0.8444   |
| 0.0028        | 15.0  | 90   | 0.7939          | 0.8444   |
| 0.0028        | 16.0  | 96   | 0.8240          | 0.8444   |
| 0.0013        | 17.0  | 102  | 0.8242          | 0.8222   |
| 0.0013        | 18.0  | 108  | 0.8443          | 0.8444   |
| 0.0014        | 19.0  | 114  | 0.8393          | 0.8444   |
| 0.0012        | 20.0  | 120  | 0.9165          | 0.8222   |
| 0.0012        | 21.0  | 126  | 0.8985          | 0.8222   |
| 0.0008        | 22.0  | 132  | 0.9053          | 0.8222   |
| 0.0008        | 23.0  | 138  | 0.9182          | 0.8222   |
| 0.0007        | 24.0  | 144  | 0.9131          | 0.8222   |
| 0.0007        | 25.0  | 150  | 0.9205          | 0.8222   |
| 0.0007        | 26.0  | 156  | 0.9165          | 0.8222   |
| 0.0004        | 27.0  | 162  | 0.9119          | 0.8222   |
| 0.0004        | 28.0  | 168  | 0.9185          | 0.8222   |
| 0.0005        | 29.0  | 174  | 0.9203          | 0.8222   |
| 0.0004        | 30.0  | 180  | 0.9232          | 0.8222   |
| 0.0004        | 31.0  | 186  | 0.9207          | 0.8444   |
| 0.0009        | 32.0  | 192  | 0.9256          | 0.8444   |
| 0.0009        | 33.0  | 198  | 0.9230          | 0.8444   |
| 0.0082        | 34.0  | 204  | 0.9200          | 0.8444   |
| 0.0007        | 35.0  | 210  | 0.9385          | 0.8444   |
| 0.0007        | 36.0  | 216  | 0.9350          | 0.8444   |
| 0.0005        | 37.0  | 222  | 0.9367          | 0.8444   |
| 0.0005        | 38.0  | 228  | 0.9290          | 0.8444   |
| 0.0044        | 39.0  | 234  | 0.9294          | 0.8444   |
| 0.0005        | 40.0  | 240  | 0.9330          | 0.8444   |
| 0.0005        | 41.0  | 246  | 0.9359          | 0.8444   |
| 0.0006        | 42.0  | 252  | 0.9358          | 0.8444   |
| 0.0006        | 43.0  | 258  | 0.9358          | 0.8444   |
| 0.0005        | 44.0  | 264  | 0.9358          | 0.8444   |
| 0.0007        | 45.0  | 270  | 0.9358          | 0.8444   |
| 0.0007        | 46.0  | 276  | 0.9358          | 0.8444   |
| 0.0007        | 47.0  | 282  | 0.9358          | 0.8444   |
| 0.0007        | 48.0  | 288  | 0.9358          | 0.8444   |
| 0.0006        | 49.0  | 294  | 0.9358          | 0.8444   |
| 0.0004        | 50.0  | 300  | 0.9358          | 0.8444   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
