---
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_lr001_fold1
  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: 0.4444444444444444
---

<!-- 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_lr001_fold1

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: 1.5661
- Accuracy: 0.4444

## 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
- 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    | 2.2715          | 0.2667   |
| No log        | 2.0   | 3    | 2.0269          | 0.4      |
| No log        | 2.67  | 4    | 1.6111          | 0.2889   |
| No log        | 4.0   | 6    | 1.4755          | 0.2444   |
| No log        | 4.67  | 7    | 1.3818          | 0.4667   |
| No log        | 6.0   | 9    | 1.3523          | 0.3111   |
| 1.6844        | 6.67  | 10   | 1.4010          | 0.2444   |
| 1.6844        | 8.0   | 12   | 1.2634          | 0.4444   |
| 1.6844        | 8.67  | 13   | 1.3983          | 0.4222   |
| 1.6844        | 10.0  | 15   | 1.7897          | 0.3778   |
| 1.6844        | 10.67 | 16   | 1.7305          | 0.3111   |
| 1.6844        | 12.0  | 18   | 1.3560          | 0.4667   |
| 1.6844        | 12.67 | 19   | 1.8545          | 0.4222   |
| 1.001         | 14.0  | 21   | 2.1000          | 0.3778   |
| 1.001         | 14.67 | 22   | 1.2257          | 0.4889   |
| 1.001         | 16.0  | 24   | 1.2741          | 0.4444   |
| 1.001         | 16.67 | 25   | 1.9098          | 0.3556   |
| 1.001         | 18.0  | 27   | 1.4981          | 0.3778   |
| 1.001         | 18.67 | 28   | 1.0949          | 0.4222   |
| 0.7366        | 20.0  | 30   | 1.1640          | 0.4222   |
| 0.7366        | 20.67 | 31   | 1.5156          | 0.3556   |
| 0.7366        | 22.0  | 33   | 1.8559          | 0.3556   |
| 0.7366        | 22.67 | 34   | 1.5735          | 0.4444   |
| 0.7366        | 24.0  | 36   | 1.3202          | 0.4222   |
| 0.7366        | 24.67 | 37   | 1.3837          | 0.4222   |
| 0.7366        | 26.0  | 39   | 1.6707          | 0.4      |
| 0.4908        | 26.67 | 40   | 1.8712          | 0.3778   |
| 0.4908        | 28.0  | 42   | 2.1885          | 0.3556   |
| 0.4908        | 28.67 | 43   | 2.0505          | 0.3556   |
| 0.4908        | 30.0  | 45   | 1.6855          | 0.4      |
| 0.4908        | 30.67 | 46   | 1.5304          | 0.4222   |
| 0.4908        | 32.0  | 48   | 1.5067          | 0.3778   |
| 0.4908        | 32.67 | 49   | 1.5442          | 0.4222   |
| 0.3287        | 33.33 | 50   | 1.5661          | 0.4444   |


### Framework versions

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