transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-base-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_base_sgd_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: 0.7936772046589018
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# smids_5x_deit_base_sgd_0001_fold2

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5171
- Accuracy: 0.7937

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0836        | 1.0   | 375   | 1.0885          | 0.3760   |
| 1.0675        | 2.0   | 750   | 1.0687          | 0.4210   |
| 1.0197        | 3.0   | 1125  | 1.0472          | 0.4792   |
| 1.0069        | 4.0   | 1500  | 1.0227          | 0.5291   |
| 0.9845        | 5.0   | 1875  | 0.9951          | 0.5757   |
| 0.9186        | 6.0   | 2250  | 0.9649          | 0.6023   |
| 0.8937        | 7.0   | 2625  | 0.9335          | 0.6240   |
| 0.9019        | 8.0   | 3000  | 0.9023          | 0.6306   |
| 0.8424        | 9.0   | 3375  | 0.8722          | 0.6539   |
| 0.8294        | 10.0  | 3750  | 0.8441          | 0.6755   |
| 0.7985        | 11.0  | 4125  | 0.8177          | 0.7038   |
| 0.7783        | 12.0  | 4500  | 0.7929          | 0.7138   |
| 0.7207        | 13.0  | 4875  | 0.7700          | 0.7238   |
| 0.7333        | 14.0  | 5250  | 0.7486          | 0.7321   |
| 0.7101        | 15.0  | 5625  | 0.7288          | 0.7471   |
| 0.6525        | 16.0  | 6000  | 0.7103          | 0.7554   |
| 0.6829        | 17.0  | 6375  | 0.6933          | 0.7521   |
| 0.6709        | 18.0  | 6750  | 0.6776          | 0.7604   |
| 0.637         | 19.0  | 7125  | 0.6631          | 0.7654   |
| 0.6386        | 20.0  | 7500  | 0.6498          | 0.7704   |
| 0.6136        | 21.0  | 7875  | 0.6376          | 0.7704   |
| 0.6058        | 22.0  | 8250  | 0.6264          | 0.7704   |
| 0.5884        | 23.0  | 8625  | 0.6162          | 0.7704   |
| 0.5772        | 24.0  | 9000  | 0.6067          | 0.7737   |
| 0.5613        | 25.0  | 9375  | 0.5979          | 0.7754   |
| 0.5696        | 26.0  | 9750  | 0.5898          | 0.7787   |
| 0.5932        | 27.0  | 10125 | 0.5824          | 0.7804   |
| 0.5407        | 28.0  | 10500 | 0.5756          | 0.7837   |
| 0.561         | 29.0  | 10875 | 0.5693          | 0.7837   |
| 0.5293        | 30.0  | 11250 | 0.5636          | 0.7854   |
| 0.5123        | 31.0  | 11625 | 0.5583          | 0.7870   |
| 0.5488        | 32.0  | 12000 | 0.5534          | 0.7870   |
| 0.4911        | 33.0  | 12375 | 0.5488          | 0.7870   |
| 0.5069        | 34.0  | 12750 | 0.5447          | 0.7870   |
| 0.5187        | 35.0  | 13125 | 0.5409          | 0.7887   |
| 0.5044        | 36.0  | 13500 | 0.5375          | 0.7903   |
| 0.5147        | 37.0  | 13875 | 0.5344          | 0.7903   |
| 0.4787        | 38.0  | 14250 | 0.5316          | 0.7920   |
| 0.5048        | 39.0  | 14625 | 0.5290          | 0.7920   |
| 0.5066        | 40.0  | 15000 | 0.5268          | 0.7920   |
| 0.5145        | 41.0  | 15375 | 0.5248          | 0.7937   |
| 0.4726        | 42.0  | 15750 | 0.5230          | 0.7937   |
| 0.4727        | 43.0  | 16125 | 0.5215          | 0.7937   |
| 0.5222        | 44.0  | 16500 | 0.5202          | 0.7937   |
| 0.4881        | 45.0  | 16875 | 0.5192          | 0.7937   |
| 0.5004        | 46.0  | 17250 | 0.5184          | 0.7937   |
| 0.4865        | 47.0  | 17625 | 0.5178          | 0.7937   |
| 0.4651        | 48.0  | 18000 | 0.5174          | 0.7937   |
| 0.4822        | 49.0  | 18375 | 0.5172          | 0.7937   |
| 0.4937        | 50.0  | 18750 | 0.5171          | 0.7937   |


### Framework versions

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