---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_tiny_adamax_lr0001_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: 0.6585365853658537
---

<!-- 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. -->

# hushem_1x_deit_tiny_adamax_lr0001_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: 0.8924
- Accuracy: 0.6585

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.67  | 1    | 1.9330          | 0.2439   |
| No log        | 2.0   | 3    | 1.4362          | 0.3659   |
| No log        | 2.67  | 4    | 1.3806          | 0.3902   |
| No log        | 4.0   | 6    | 1.3304          | 0.4634   |
| No log        | 4.67  | 7    | 1.3017          | 0.4390   |
| No log        | 6.0   | 9    | 1.1836          | 0.4878   |
| 1.2323        | 6.67  | 10   | 1.1688          | 0.5610   |
| 1.2323        | 8.0   | 12   | 1.1361          | 0.5366   |
| 1.2323        | 8.67  | 13   | 1.1291          | 0.5366   |
| 1.2323        | 10.0  | 15   | 1.0782          | 0.6098   |
| 1.2323        | 10.67 | 16   | 1.0358          | 0.6585   |
| 1.2323        | 12.0  | 18   | 1.0020          | 0.6098   |
| 1.2323        | 12.67 | 19   | 1.0059          | 0.6098   |
| 0.3527        | 14.0  | 21   | 0.9293          | 0.6098   |
| 0.3527        | 14.67 | 22   | 0.9162          | 0.6341   |
| 0.3527        | 16.0  | 24   | 0.9233          | 0.6098   |
| 0.3527        | 16.67 | 25   | 0.9213          | 0.6098   |
| 0.3527        | 18.0  | 27   | 0.9193          | 0.6098   |
| 0.3527        | 18.67 | 28   | 0.9345          | 0.6098   |
| 0.04          | 20.0  | 30   | 0.8872          | 0.6585   |
| 0.04          | 20.67 | 31   | 0.8549          | 0.6829   |
| 0.04          | 22.0  | 33   | 0.8221          | 0.6829   |
| 0.04          | 22.67 | 34   | 0.8117          | 0.7073   |
| 0.04          | 24.0  | 36   | 0.8041          | 0.7561   |
| 0.04          | 24.67 | 37   | 0.8128          | 0.7561   |
| 0.04          | 26.0  | 39   | 0.8413          | 0.6829   |
| 0.0062        | 26.67 | 40   | 0.8565          | 0.6585   |
| 0.0062        | 28.0  | 42   | 0.8789          | 0.6585   |
| 0.0062        | 28.67 | 43   | 0.8864          | 0.6585   |
| 0.0062        | 30.0  | 45   | 0.8920          | 0.6585   |
| 0.0062        | 30.67 | 46   | 0.8925          | 0.6585   |
| 0.0062        | 32.0  | 48   | 0.8929          | 0.6585   |
| 0.0062        | 32.67 | 49   | 0.8927          | 0.6585   |
| 0.0031        | 33.33 | 50   | 0.8924          | 0.6585   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1