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_10x_deit_base_sgd_001_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: 0.9083333333333333
---

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# smids_10x_deit_base_sgd_001_fold3

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: 0.2607
- Accuracy: 0.9083

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5671        | 1.0   | 750   | 0.5928          | 0.7967   |
| 0.408         | 2.0   | 1500  | 0.4283          | 0.8517   |
| 0.3447        | 3.0   | 2250  | 0.3742          | 0.8633   |
| 0.3088        | 4.0   | 3000  | 0.3475          | 0.8683   |
| 0.2979        | 5.0   | 3750  | 0.3269          | 0.8733   |
| 0.2962        | 6.0   | 4500  | 0.3183          | 0.8767   |
| 0.2557        | 7.0   | 5250  | 0.3059          | 0.8817   |
| 0.2555        | 8.0   | 6000  | 0.2957          | 0.8817   |
| 0.2367        | 9.0   | 6750  | 0.2914          | 0.885    |
| 0.1949        | 10.0  | 7500  | 0.2859          | 0.8917   |
| 0.2488        | 11.0  | 8250  | 0.2846          | 0.8917   |
| 0.2475        | 12.0  | 9000  | 0.2777          | 0.895    |
| 0.1828        | 13.0  | 9750  | 0.2753          | 0.8983   |
| 0.2439        | 14.0  | 10500 | 0.2718          | 0.9017   |
| 0.2473        | 15.0  | 11250 | 0.2704          | 0.895    |
| 0.1928        | 16.0  | 12000 | 0.2696          | 0.895    |
| 0.1843        | 17.0  | 12750 | 0.2710          | 0.8983   |
| 0.2029        | 18.0  | 13500 | 0.2625          | 0.9033   |
| 0.2035        | 19.0  | 14250 | 0.2665          | 0.9      |
| 0.1744        | 20.0  | 15000 | 0.2677          | 0.905    |
| 0.152         | 21.0  | 15750 | 0.2612          | 0.9033   |
| 0.1898        | 22.0  | 16500 | 0.2631          | 0.9      |
| 0.1983        | 23.0  | 17250 | 0.2648          | 0.9067   |
| 0.1746        | 24.0  | 18000 | 0.2651          | 0.9067   |
| 0.2045        | 25.0  | 18750 | 0.2633          | 0.9067   |
| 0.1969        | 26.0  | 19500 | 0.2578          | 0.9067   |
| 0.1227        | 27.0  | 20250 | 0.2593          | 0.91     |
| 0.1518        | 28.0  | 21000 | 0.2610          | 0.9083   |
| 0.1661        | 29.0  | 21750 | 0.2607          | 0.9067   |
| 0.1698        | 30.0  | 22500 | 0.2600          | 0.9083   |
| 0.1513        | 31.0  | 23250 | 0.2624          | 0.9067   |
| 0.1181        | 32.0  | 24000 | 0.2595          | 0.9083   |
| 0.1772        | 33.0  | 24750 | 0.2601          | 0.9083   |
| 0.1745        | 34.0  | 25500 | 0.2608          | 0.9083   |
| 0.1241        | 35.0  | 26250 | 0.2599          | 0.9083   |
| 0.1459        | 36.0  | 27000 | 0.2607          | 0.9083   |
| 0.1333        | 37.0  | 27750 | 0.2607          | 0.9083   |
| 0.1934        | 38.0  | 28500 | 0.2605          | 0.905    |
| 0.1357        | 39.0  | 29250 | 0.2594          | 0.91     |
| 0.1781        | 40.0  | 30000 | 0.2597          | 0.91     |
| 0.1473        | 41.0  | 30750 | 0.2601          | 0.91     |
| 0.1773        | 42.0  | 31500 | 0.2596          | 0.9083   |
| 0.1488        | 43.0  | 32250 | 0.2600          | 0.9083   |
| 0.1451        | 44.0  | 33000 | 0.2615          | 0.9083   |
| 0.1365        | 45.0  | 33750 | 0.2606          | 0.9083   |
| 0.1144        | 46.0  | 34500 | 0.2619          | 0.9067   |
| 0.1714        | 47.0  | 35250 | 0.2607          | 0.9083   |
| 0.1189        | 48.0  | 36000 | 0.2607          | 0.9083   |
| 0.1679        | 49.0  | 36750 | 0.2607          | 0.9083   |
| 0.1606        | 50.0  | 37500 | 0.2607          | 0.9083   |


### Framework versions

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