---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_1x_deit_tiny_adamax_00001_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: 0.88
---

<!-- 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_1x_deit_tiny_adamax_00001_fold5

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: 0.6353
- Accuracy: 0.88

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7267        | 1.0   | 75   | 0.6452          | 0.735    |
| 0.4663        | 2.0   | 150  | 0.4479          | 0.8267   |
| 0.3889        | 3.0   | 225  | 0.3768          | 0.8467   |
| 0.4049        | 4.0   | 300  | 0.3517          | 0.8567   |
| 0.3455        | 5.0   | 375  | 0.3282          | 0.8733   |
| 0.2551        | 6.0   | 450  | 0.3120          | 0.8783   |
| 0.253         | 7.0   | 525  | 0.3013          | 0.8733   |
| 0.2149        | 8.0   | 600  | 0.3148          | 0.8733   |
| 0.1593        | 9.0   | 675  | 0.3191          | 0.8733   |
| 0.1223        | 10.0  | 750  | 0.3121          | 0.875    |
| 0.186         | 11.0  | 825  | 0.3224          | 0.875    |
| 0.0958        | 12.0  | 900  | 0.3269          | 0.875    |
| 0.1004        | 13.0  | 975  | 0.3361          | 0.8833   |
| 0.0897        | 14.0  | 1050 | 0.3443          | 0.8833   |
| 0.0668        | 15.0  | 1125 | 0.4038          | 0.8683   |
| 0.0352        | 16.0  | 1200 | 0.3877          | 0.865    |
| 0.026         | 17.0  | 1275 | 0.3941          | 0.88     |
| 0.0445        | 18.0  | 1350 | 0.4318          | 0.8767   |
| 0.0301        | 19.0  | 1425 | 0.4418          | 0.8783   |
| 0.0287        | 20.0  | 1500 | 0.4505          | 0.88     |
| 0.0241        | 21.0  | 1575 | 0.4895          | 0.8817   |
| 0.01          | 22.0  | 1650 | 0.4921          | 0.8833   |
| 0.0214        | 23.0  | 1725 | 0.5270          | 0.88     |
| 0.0229        | 24.0  | 1800 | 0.5245          | 0.8767   |
| 0.0113        | 25.0  | 1875 | 0.5406          | 0.8767   |
| 0.0032        | 26.0  | 1950 | 0.5510          | 0.8817   |
| 0.0224        | 27.0  | 2025 | 0.5698          | 0.8783   |
| 0.029         | 28.0  | 2100 | 0.5630          | 0.8733   |
| 0.0174        | 29.0  | 2175 | 0.5715          | 0.87     |
| 0.0132        | 30.0  | 2250 | 0.5787          | 0.8767   |
| 0.0005        | 31.0  | 2325 | 0.6094          | 0.8783   |
| 0.0004        | 32.0  | 2400 | 0.5908          | 0.875    |
| 0.0169        | 33.0  | 2475 | 0.6294          | 0.8767   |
| 0.0005        | 34.0  | 2550 | 0.6011          | 0.875    |
| 0.0188        | 35.0  | 2625 | 0.6103          | 0.88     |
| 0.0003        | 36.0  | 2700 | 0.6040          | 0.8783   |
| 0.0093        | 37.0  | 2775 | 0.6346          | 0.875    |
| 0.0003        | 38.0  | 2850 | 0.6319          | 0.875    |
| 0.0004        | 39.0  | 2925 | 0.6321          | 0.875    |
| 0.0005        | 40.0  | 3000 | 0.6332          | 0.875    |
| 0.0131        | 41.0  | 3075 | 0.6261          | 0.88     |
| 0.0004        | 42.0  | 3150 | 0.6258          | 0.8783   |
| 0.0183        | 43.0  | 3225 | 0.6307          | 0.8783   |
| 0.0032        | 44.0  | 3300 | 0.6338          | 0.8783   |
| 0.0145        | 45.0  | 3375 | 0.6347          | 0.8817   |
| 0.0002        | 46.0  | 3450 | 0.6408          | 0.88     |
| 0.0126        | 47.0  | 3525 | 0.6363          | 0.8817   |
| 0.0174        | 48.0  | 3600 | 0.6374          | 0.8833   |
| 0.0002        | 49.0  | 3675 | 0.6360          | 0.8817   |
| 0.0129        | 50.0  | 3750 | 0.6353          | 0.88     |


### Framework versions

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