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_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: 0.5033333333333333
---

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# smids_1x_deit_small_sgd_00001_fold4

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: 1.0280
- Accuracy: 0.5033

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0927        | 1.0   | 75   | 1.0671          | 0.43     |
| 1.0963        | 2.0   | 150  | 1.0650          | 0.4317   |
| 1.0708        | 3.0   | 225  | 1.0630          | 0.43     |
| 1.0487        | 4.0   | 300  | 1.0611          | 0.4317   |
| 1.0896        | 5.0   | 375  | 1.0592          | 0.435    |
| 1.0673        | 6.0   | 450  | 1.0575          | 0.435    |
| 1.067         | 7.0   | 525  | 1.0559          | 0.4367   |
| 1.0743        | 8.0   | 600  | 1.0543          | 0.4417   |
| 1.0607        | 9.0   | 675  | 1.0527          | 0.445    |
| 1.058         | 10.0  | 750  | 1.0512          | 0.4483   |
| 1.0598        | 11.0  | 825  | 1.0498          | 0.4483   |
| 1.0745        | 12.0  | 900  | 1.0485          | 0.45     |
| 1.0539        | 13.0  | 975  | 1.0472          | 0.45     |
| 1.0532        | 14.0  | 1050 | 1.0460          | 0.455    |
| 1.0553        | 15.0  | 1125 | 1.0448          | 0.4567   |
| 1.0605        | 16.0  | 1200 | 1.0437          | 0.465    |
| 1.0719        | 17.0  | 1275 | 1.0426          | 0.4667   |
| 1.0217        | 18.0  | 1350 | 1.0415          | 0.465    |
| 1.0569        | 19.0  | 1425 | 1.0406          | 0.4617   |
| 1.0748        | 20.0  | 1500 | 1.0396          | 0.4633   |
| 1.0485        | 21.0  | 1575 | 1.0388          | 0.4633   |
| 1.0436        | 22.0  | 1650 | 1.0379          | 0.465    |
| 1.0728        | 23.0  | 1725 | 1.0371          | 0.47     |
| 1.0532        | 24.0  | 1800 | 1.0364          | 0.475    |
| 1.0361        | 25.0  | 1875 | 1.0357          | 0.4767   |
| 1.0392        | 26.0  | 1950 | 1.0350          | 0.475    |
| 1.029         | 27.0  | 2025 | 1.0343          | 0.4767   |
| 1.0447        | 28.0  | 2100 | 1.0337          | 0.4733   |
| 1.0454        | 29.0  | 2175 | 1.0332          | 0.4783   |
| 1.0483        | 30.0  | 2250 | 1.0326          | 0.4783   |
| 1.0373        | 31.0  | 2325 | 1.0321          | 0.4833   |
| 1.0733        | 32.0  | 2400 | 1.0316          | 0.485    |
| 1.0534        | 33.0  | 2475 | 1.0312          | 0.4883   |
| 1.043         | 34.0  | 2550 | 1.0308          | 0.4883   |
| 1.0232        | 35.0  | 2625 | 1.0304          | 0.4883   |
| 1.0268        | 36.0  | 2700 | 1.0300          | 0.4917   |
| 1.0287        | 37.0  | 2775 | 1.0297          | 0.495    |
| 1.0612        | 38.0  | 2850 | 1.0294          | 0.4967   |
| 1.0429        | 39.0  | 2925 | 1.0292          | 0.4983   |
| 1.0312        | 40.0  | 3000 | 1.0290          | 0.4983   |
| 1.0436        | 41.0  | 3075 | 1.0287          | 0.5      |
| 1.0377        | 42.0  | 3150 | 1.0286          | 0.5      |
| 1.046         | 43.0  | 3225 | 1.0284          | 0.5017   |
| 1.0455        | 44.0  | 3300 | 1.0283          | 0.5033   |
| 1.0485        | 45.0  | 3375 | 1.0282          | 0.5033   |
| 1.0401        | 46.0  | 3450 | 1.0281          | 0.5033   |
| 1.0459        | 47.0  | 3525 | 1.0280          | 0.5033   |
| 1.0359        | 48.0  | 3600 | 1.0280          | 0.5033   |
| 1.0504        | 49.0  | 3675 | 1.0280          | 0.5033   |
| 1.0198        | 50.0  | 3750 | 1.0280          | 0.5033   |


### Framework versions

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