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_001_fold5
  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: 0.89
---

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# smids_5x_deit_base_sgd_001_fold5

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: 0.2653
- Accuracy: 0.89

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.816         | 1.0   | 375   | 0.8299          | 0.6983   |
| 0.6079        | 2.0   | 750   | 0.5776          | 0.79     |
| 0.4275        | 3.0   | 1125  | 0.4639          | 0.815    |
| 0.435         | 4.0   | 1500  | 0.4090          | 0.8217   |
| 0.3764        | 5.0   | 1875  | 0.3770          | 0.83     |
| 0.3648        | 6.0   | 2250  | 0.3546          | 0.8333   |
| 0.2762        | 7.0   | 2625  | 0.3403          | 0.8383   |
| 0.295         | 8.0   | 3000  | 0.3285          | 0.85     |
| 0.2923        | 9.0   | 3375  | 0.3205          | 0.8517   |
| 0.2438        | 10.0  | 3750  | 0.3147          | 0.855    |
| 0.2438        | 11.0  | 4125  | 0.3070          | 0.86     |
| 0.2431        | 12.0  | 4500  | 0.3020          | 0.8667   |
| 0.2192        | 13.0  | 4875  | 0.2993          | 0.865    |
| 0.2554        | 14.0  | 5250  | 0.2960          | 0.8717   |
| 0.2885        | 15.0  | 5625  | 0.2913          | 0.8783   |
| 0.1896        | 16.0  | 6000  | 0.2879          | 0.88     |
| 0.3057        | 17.0  | 6375  | 0.2870          | 0.8733   |
| 0.2444        | 18.0  | 6750  | 0.2845          | 0.88     |
| 0.2179        | 19.0  | 7125  | 0.2810          | 0.8783   |
| 0.1663        | 20.0  | 7500  | 0.2810          | 0.8783   |
| 0.2067        | 21.0  | 7875  | 0.2763          | 0.8833   |
| 0.197         | 22.0  | 8250  | 0.2779          | 0.8817   |
| 0.3036        | 23.0  | 8625  | 0.2762          | 0.8817   |
| 0.2123        | 24.0  | 9000  | 0.2743          | 0.8817   |
| 0.2471        | 25.0  | 9375  | 0.2741          | 0.8783   |
| 0.2004        | 26.0  | 9750  | 0.2742          | 0.88     |
| 0.2358        | 27.0  | 10125 | 0.2736          | 0.88     |
| 0.2033        | 28.0  | 10500 | 0.2701          | 0.88     |
| 0.2195        | 29.0  | 10875 | 0.2687          | 0.8833   |
| 0.1807        | 30.0  | 11250 | 0.2702          | 0.8817   |
| 0.2285        | 31.0  | 11625 | 0.2693          | 0.8833   |
| 0.2043        | 32.0  | 12000 | 0.2686          | 0.8883   |
| 0.2113        | 33.0  | 12375 | 0.2682          | 0.89     |
| 0.253         | 34.0  | 12750 | 0.2667          | 0.8883   |
| 0.1854        | 35.0  | 13125 | 0.2674          | 0.885    |
| 0.1505        | 36.0  | 13500 | 0.2668          | 0.8867   |
| 0.2105        | 37.0  | 13875 | 0.2666          | 0.885    |
| 0.184         | 38.0  | 14250 | 0.2661          | 0.8867   |
| 0.1486        | 39.0  | 14625 | 0.2664          | 0.8867   |
| 0.2231        | 40.0  | 15000 | 0.2660          | 0.8883   |
| 0.185         | 41.0  | 15375 | 0.2658          | 0.8867   |
| 0.166         | 42.0  | 15750 | 0.2657          | 0.8883   |
| 0.1944        | 43.0  | 16125 | 0.2649          | 0.8933   |
| 0.1752        | 44.0  | 16500 | 0.2656          | 0.8883   |
| 0.1859        | 45.0  | 16875 | 0.2655          | 0.89     |
| 0.2257        | 46.0  | 17250 | 0.2655          | 0.8867   |
| 0.2194        | 47.0  | 17625 | 0.2653          | 0.8883   |
| 0.1536        | 48.0  | 18000 | 0.2653          | 0.8883   |
| 0.1835        | 49.0  | 18375 | 0.2652          | 0.89     |
| 0.1852        | 50.0  | 18750 | 0.2653          | 0.89     |


### Framework versions

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