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_3x_beit_base_adamax_0001_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: 0.8901830282861897
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# smids_3x_beit_base_adamax_0001_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: 0.9159
- Accuracy: 0.8902

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3022        | 1.0   | 225   | 0.2725          | 0.8935   |
| 0.178         | 2.0   | 450   | 0.3008          | 0.8852   |
| 0.0845        | 3.0   | 675   | 0.4512          | 0.8686   |
| 0.0684        | 4.0   | 900   | 0.3867          | 0.8852   |
| 0.0813        | 5.0   | 1125  | 0.6871          | 0.8686   |
| 0.046         | 6.0   | 1350  | 0.5810          | 0.8802   |
| 0.0277        | 7.0   | 1575  | 0.6980          | 0.8769   |
| 0.0479        | 8.0   | 1800  | 0.6091          | 0.9002   |
| 0.0027        | 9.0   | 2025  | 0.7134          | 0.8952   |
| 0.0066        | 10.0  | 2250  | 0.6880          | 0.8952   |
| 0.0321        | 11.0  | 2475  | 0.6460          | 0.8902   |
| 0.0014        | 12.0  | 2700  | 0.7817          | 0.8935   |
| 0.0002        | 13.0  | 2925  | 0.6934          | 0.8935   |
| 0.021         | 14.0  | 3150  | 0.7639          | 0.9002   |
| 0.0064        | 15.0  | 3375  | 0.7684          | 0.8918   |
| 0.0006        | 16.0  | 3600  | 0.8221          | 0.8802   |
| 0.0143        | 17.0  | 3825  | 0.7209          | 0.8902   |
| 0.0001        | 18.0  | 4050  | 0.6982          | 0.8918   |
| 0.0001        | 19.0  | 4275  | 0.7862          | 0.9002   |
| 0.0027        | 20.0  | 4500  | 0.7966          | 0.8819   |
| 0.0001        | 21.0  | 4725  | 0.8116          | 0.8918   |
| 0.007         | 22.0  | 4950  | 0.9903          | 0.8869   |
| 0.0034        | 23.0  | 5175  | 0.8839          | 0.8935   |
| 0.0           | 24.0  | 5400  | 0.8613          | 0.8885   |
| 0.005         | 25.0  | 5625  | 0.8407          | 0.8935   |
| 0.0001        | 26.0  | 5850  | 0.8776          | 0.8968   |
| 0.0           | 27.0  | 6075  | 0.8976          | 0.8885   |
| 0.0           | 28.0  | 6300  | 0.8439          | 0.8918   |
| 0.0002        | 29.0  | 6525  | 0.8561          | 0.9035   |
| 0.0034        | 30.0  | 6750  | 0.8784          | 0.9035   |
| 0.0001        | 31.0  | 6975  | 1.0043          | 0.8835   |
| 0.0           | 32.0  | 7200  | 0.9310          | 0.8968   |
| 0.0001        | 33.0  | 7425  | 0.9435          | 0.8985   |
| 0.0002        | 34.0  | 7650  | 0.9440          | 0.8885   |
| 0.0           | 35.0  | 7875  | 0.9680          | 0.8869   |
| 0.0033        | 36.0  | 8100  | 0.9425          | 0.8952   |
| 0.0           | 37.0  | 8325  | 0.8592          | 0.9018   |
| 0.0001        | 38.0  | 8550  | 0.8640          | 0.8985   |
| 0.0029        | 39.0  | 8775  | 0.9094          | 0.8935   |
| 0.0           | 40.0  | 9000  | 0.8962          | 0.8968   |
| 0.0           | 41.0  | 9225  | 0.9188          | 0.8935   |
| 0.0           | 42.0  | 9450  | 0.9151          | 0.8968   |
| 0.0           | 43.0  | 9675  | 0.9033          | 0.9002   |
| 0.0           | 44.0  | 9900  | 0.9139          | 0.8968   |
| 0.0004        | 45.0  | 10125 | 0.9130          | 0.8968   |
| 0.0           | 46.0  | 10350 | 0.9128          | 0.8985   |
| 0.0           | 47.0  | 10575 | 0.9094          | 0.8952   |
| 0.0           | 48.0  | 10800 | 0.9134          | 0.8952   |
| 0.0032        | 49.0  | 11025 | 0.9151          | 0.8902   |
| 0.0002        | 50.0  | 11250 | 0.9159          | 0.8902   |


### Framework versions

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