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: smids_1x_beit_base_sgd_001_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: 0.8533333333333334
---

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# smids_1x_beit_base_sgd_001_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: 0.3889
- Accuracy: 0.8533

## 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: 0.001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0889        | 1.0   | 75   | 1.0669          | 0.4483   |
| 0.8655        | 2.0   | 150  | 0.8559          | 0.645    |
| 0.7785        | 3.0   | 225  | 0.7294          | 0.7017   |
| 0.7379        | 4.0   | 300  | 0.6722          | 0.7167   |
| 0.7102        | 5.0   | 375  | 0.6344          | 0.7367   |
| 0.679         | 6.0   | 450  | 0.6029          | 0.7583   |
| 0.6616        | 7.0   | 525  | 0.5757          | 0.7667   |
| 0.6078        | 8.0   | 600  | 0.5519          | 0.7983   |
| 0.6427        | 9.0   | 675  | 0.5357          | 0.7867   |
| 0.5452        | 10.0  | 750  | 0.5237          | 0.7983   |
| 0.5678        | 11.0  | 825  | 0.5091          | 0.7933   |
| 0.5374        | 12.0  | 900  | 0.4984          | 0.8083   |
| 0.5811        | 13.0  | 975  | 0.4866          | 0.8033   |
| 0.5027        | 14.0  | 1050 | 0.4734          | 0.81     |
| 0.4787        | 15.0  | 1125 | 0.4709          | 0.805    |
| 0.4828        | 16.0  | 1200 | 0.4644          | 0.8117   |
| 0.4631        | 17.0  | 1275 | 0.4587          | 0.81     |
| 0.4343        | 18.0  | 1350 | 0.4468          | 0.82     |
| 0.4904        | 19.0  | 1425 | 0.4435          | 0.82     |
| 0.4433        | 20.0  | 1500 | 0.4398          | 0.8167   |
| 0.4461        | 21.0  | 1575 | 0.4318          | 0.8283   |
| 0.3894        | 22.0  | 1650 | 0.4361          | 0.8217   |
| 0.4398        | 23.0  | 1725 | 0.4269          | 0.8333   |
| 0.4433        | 24.0  | 1800 | 0.4213          | 0.835    |
| 0.4253        | 25.0  | 1875 | 0.4182          | 0.8333   |
| 0.4141        | 26.0  | 1950 | 0.4208          | 0.835    |
| 0.4307        | 27.0  | 2025 | 0.4086          | 0.8433   |
| 0.4132        | 28.0  | 2100 | 0.4078          | 0.8417   |
| 0.41          | 29.0  | 2175 | 0.4074          | 0.8467   |
| 0.4398        | 30.0  | 2250 | 0.4048          | 0.8383   |
| 0.3394        | 31.0  | 2325 | 0.4047          | 0.8383   |
| 0.4174        | 32.0  | 2400 | 0.4015          | 0.8433   |
| 0.4379        | 33.0  | 2475 | 0.3992          | 0.8433   |
| 0.3836        | 34.0  | 2550 | 0.3988          | 0.845    |
| 0.3824        | 35.0  | 2625 | 0.3986          | 0.845    |
| 0.3571        | 36.0  | 2700 | 0.3978          | 0.8433   |
| 0.348         | 37.0  | 2775 | 0.3932          | 0.8483   |
| 0.3852        | 38.0  | 2850 | 0.3961          | 0.8433   |
| 0.4099        | 39.0  | 2925 | 0.3932          | 0.8483   |
| 0.4003        | 40.0  | 3000 | 0.3917          | 0.8533   |
| 0.3921        | 41.0  | 3075 | 0.3931          | 0.8483   |
| 0.3545        | 42.0  | 3150 | 0.3904          | 0.8533   |
| 0.3816        | 43.0  | 3225 | 0.3916          | 0.8467   |
| 0.3789        | 44.0  | 3300 | 0.3906          | 0.8533   |
| 0.373         | 45.0  | 3375 | 0.3905          | 0.85     |
| 0.3767        | 46.0  | 3450 | 0.3894          | 0.8533   |
| 0.3814        | 47.0  | 3525 | 0.3896          | 0.8517   |
| 0.3984        | 48.0  | 3600 | 0.3899          | 0.85     |
| 0.3749        | 49.0  | 3675 | 0.3891          | 0.8533   |
| 0.3605        | 50.0  | 3750 | 0.3889          | 0.8533   |


### Framework versions

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