transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-tiny-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_10x_deit_tiny_rms_001_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: 0.8019966722129783
---

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# smids_10x_deit_tiny_rms_001_fold2

This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1068
- Accuracy: 0.8020

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8332        | 1.0   | 750   | 0.7567          | 0.6090   |
| 0.7395        | 2.0   | 1500  | 0.7599          | 0.6123   |
| 0.682         | 3.0   | 2250  | 0.6859          | 0.6905   |
| 0.725         | 4.0   | 3000  | 0.6463          | 0.7171   |
| 0.6632        | 5.0   | 3750  | 0.6560          | 0.7238   |
| 0.5777        | 6.0   | 4500  | 0.6347          | 0.7072   |
| 0.6357        | 7.0   | 5250  | 0.6141          | 0.7321   |
| 0.595         | 8.0   | 6000  | 0.6313          | 0.7121   |
| 0.5551        | 9.0   | 6750  | 0.6406          | 0.6955   |
| 0.5544        | 10.0  | 7500  | 0.5482          | 0.7720   |
| 0.5611        | 11.0  | 8250  | 0.5288          | 0.7704   |
| 0.6632        | 12.0  | 9000  | 0.5868          | 0.7537   |
| 0.5709        | 13.0  | 9750  | 0.6149          | 0.7288   |
| 0.4511        | 14.0  | 10500 | 0.4977          | 0.8020   |
| 0.4295        | 15.0  | 11250 | 0.5625          | 0.7770   |
| 0.4618        | 16.0  | 12000 | 0.5273          | 0.7837   |
| 0.4342        | 17.0  | 12750 | 0.5207          | 0.7804   |
| 0.4253        | 18.0  | 13500 | 0.5301          | 0.7720   |
| 0.4352        | 19.0  | 14250 | 0.5236          | 0.7754   |
| 0.418         | 20.0  | 15000 | 0.5318          | 0.7804   |
| 0.4496        | 21.0  | 15750 | 0.5216          | 0.7970   |
| 0.4003        | 22.0  | 16500 | 0.5391          | 0.7720   |
| 0.4411        | 23.0  | 17250 | 0.4904          | 0.8003   |
| 0.3266        | 24.0  | 18000 | 0.5436          | 0.7854   |
| 0.3733        | 25.0  | 18750 | 0.6780          | 0.7521   |
| 0.3536        | 26.0  | 19500 | 0.5100          | 0.8003   |
| 0.4154        | 27.0  | 20250 | 0.5545          | 0.8020   |
| 0.414         | 28.0  | 21000 | 0.5841          | 0.7937   |
| 0.3146        | 29.0  | 21750 | 0.5867          | 0.7887   |
| 0.3401        | 30.0  | 22500 | 0.5923          | 0.7987   |
| 0.2331        | 31.0  | 23250 | 0.6367          | 0.7837   |
| 0.238         | 32.0  | 24000 | 0.6276          | 0.8070   |
| 0.209         | 33.0  | 24750 | 0.6337          | 0.8070   |
| 0.2121        | 34.0  | 25500 | 0.6961          | 0.7854   |
| 0.2544        | 35.0  | 26250 | 0.7936          | 0.7870   |
| 0.2442        | 36.0  | 27000 | 0.7270          | 0.7970   |
| 0.2459        | 37.0  | 27750 | 0.7553          | 0.8020   |
| 0.1428        | 38.0  | 28500 | 0.8600          | 0.7987   |
| 0.0788        | 39.0  | 29250 | 0.9727          | 0.7937   |
| 0.1811        | 40.0  | 30000 | 1.0324          | 0.7937   |
| 0.1405        | 41.0  | 30750 | 1.0037          | 0.8103   |
| 0.1282        | 42.0  | 31500 | 1.1830          | 0.7937   |
| 0.0664        | 43.0  | 32250 | 1.2624          | 0.7970   |
| 0.04          | 44.0  | 33000 | 1.4942          | 0.7987   |
| 0.0582        | 45.0  | 33750 | 1.4631          | 0.8103   |
| 0.0738        | 46.0  | 34500 | 1.6687          | 0.8120   |
| 0.0282        | 47.0  | 35250 | 1.8321          | 0.8087   |
| 0.0021        | 48.0  | 36000 | 1.9181          | 0.8087   |
| 0.01          | 49.0  | 36750 | 2.0036          | 0.8037   |
| 0.0004        | 50.0  | 37500 | 2.1068          | 0.8020   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
