transformers tensorboard safetensors vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-small-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_1x_deit_small_adamax_00001_fold3
  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: 0.9033333333333333
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# smids_1x_deit_small_adamax_00001_fold3

This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6007
- Accuracy: 0.9033

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5827        | 1.0   | 75   | 0.5406          | 0.805    |
| 0.4124        | 2.0   | 150  | 0.3981          | 0.855    |
| 0.3474        | 3.0   | 225  | 0.3546          | 0.8683   |
| 0.2752        | 4.0   | 300  | 0.3424          | 0.8733   |
| 0.1916        | 5.0   | 375  | 0.3180          | 0.8767   |
| 0.1614        | 6.0   | 450  | 0.3047          | 0.8933   |
| 0.176         | 7.0   | 525  | 0.3087          | 0.8967   |
| 0.0932        | 8.0   | 600  | 0.3077          | 0.905    |
| 0.1095        | 9.0   | 675  | 0.3172          | 0.8983   |
| 0.0598        | 10.0  | 750  | 0.3393          | 0.8933   |
| 0.0561        | 11.0  | 825  | 0.3389          | 0.8983   |
| 0.0304        | 12.0  | 900  | 0.3510          | 0.9017   |
| 0.0312        | 13.0  | 975  | 0.3659          | 0.8967   |
| 0.0138        | 14.0  | 1050 | 0.3876          | 0.9033   |
| 0.0066        | 15.0  | 1125 | 0.4169          | 0.895    |
| 0.0031        | 16.0  | 1200 | 0.4314          | 0.8933   |
| 0.0025        | 17.0  | 1275 | 0.4363          | 0.9017   |
| 0.0143        | 18.0  | 1350 | 0.4488          | 0.9017   |
| 0.0193        | 19.0  | 1425 | 0.4765          | 0.9017   |
| 0.0077        | 20.0  | 1500 | 0.5000          | 0.9017   |
| 0.001         | 21.0  | 1575 | 0.4881          | 0.8967   |
| 0.0006        | 22.0  | 1650 | 0.5102          | 0.8967   |
| 0.0114        | 23.0  | 1725 | 0.5087          | 0.9017   |
| 0.0005        | 24.0  | 1800 | 0.5357          | 0.9017   |
| 0.0004        | 25.0  | 1875 | 0.5221          | 0.9017   |
| 0.0091        | 26.0  | 1950 | 0.5331          | 0.8983   |
| 0.0004        | 27.0  | 2025 | 0.5349          | 0.8983   |
| 0.0004        | 28.0  | 2100 | 0.5415          | 0.9017   |
| 0.0003        | 29.0  | 2175 | 0.5413          | 0.9017   |
| 0.0003        | 30.0  | 2250 | 0.5492          | 0.9      |
| 0.0003        | 31.0  | 2325 | 0.5599          | 0.9017   |
| 0.0003        | 32.0  | 2400 | 0.5614          | 0.9      |
| 0.0002        | 33.0  | 2475 | 0.5598          | 0.9      |
| 0.0063        | 34.0  | 2550 | 0.5669          | 0.9033   |
| 0.0068        | 35.0  | 2625 | 0.5658          | 0.905    |
| 0.0129        | 36.0  | 2700 | 0.5827          | 0.9017   |
| 0.002         | 37.0  | 2775 | 0.5827          | 0.9017   |
| 0.0002        | 38.0  | 2850 | 0.5886          | 0.905    |
| 0.0002        | 39.0  | 2925 | 0.5846          | 0.8983   |
| 0.0002        | 40.0  | 3000 | 0.5897          | 0.905    |
| 0.0002        | 41.0  | 3075 | 0.5926          | 0.905    |
| 0.0007        | 42.0  | 3150 | 0.5977          | 0.9017   |
| 0.0036        | 43.0  | 3225 | 0.5932          | 0.905    |
| 0.0002        | 44.0  | 3300 | 0.5979          | 0.9017   |
| 0.01          | 45.0  | 3375 | 0.6035          | 0.9033   |
| 0.0096        | 46.0  | 3450 | 0.6006          | 0.9033   |
| 0.0033        | 47.0  | 3525 | 0.6008          | 0.905    |
| 0.0001        | 48.0  | 3600 | 0.5996          | 0.9033   |
| 0.0001        | 49.0  | 3675 | 0.6014          | 0.9033   |
| 0.003         | 50.0  | 3750 | 0.6007          | 0.9033   |


### Framework versions

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