---
base_model: MBZUAI/swiftformer-xs
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swiftformer-xs-DMAE
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.45652173913043476
---

<!-- 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. -->

# swiftformer-xs-DMAE

This model is a fine-tuned version of [MBZUAI/swiftformer-xs](https://huggingface.co/MBZUAI/swiftformer-xs) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 67319515540793508675715072.0000
- Accuracy: 0.4565

## 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: 0.015
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40

### Training results

| Training Loss                   | Epoch | Step | Validation Loss                 | Accuracy |
|:-------------------------------:|:-----:|:----:|:-------------------------------:|:--------:|
| No log                          | 0.86  | 3    | 67319515540793508675715072.0000 | 0.6739   |
| No log                          | 2.0   | 7    | 67319515540793508675715072.0000 | 0.1087   |
| 65998362497246039927422976.0000 | 2.86  | 10   | 67319515540793508675715072.0000 | 0.3261   |
| 65998362497246039927422976.0000 | 4.0   | 14   | 67319515540793508675715072.0000 | 0.4565   |
| 65998362497246039927422976.0000 | 4.86  | 17   | 67319515540793508675715072.0000 | 0.3261   |
| 69095061697085437542137856.0000 | 6.0   | 21   | 67319515540793508675715072.0000 | 0.4783   |
| 69095061697085437542137856.0000 | 6.86  | 24   | 67319515540793508675715072.0000 | 0.4565   |
| 69095061697085437542137856.0000 | 8.0   | 28   | 67319515540793508675715072.0000 | 0.4565   |
| 77610986341318196513996800.0000 | 8.86  | 31   | 67319515540793508675715072.0000 | 0.5      |
| 77610986341318196513996800.0000 | 10.0  | 35   | 67319515540793508675715072.0000 | 0.3478   |
| 77610986341318196513996800.0000 | 10.86 | 38   | 67319515540793508675715072.0000 | 0.3478   |
| 57288905682238366283726848.0000 | 12.0  | 42   | 67319515540793508675715072.0000 | 0.3478   |
| 57288905682238366283726848.0000 | 12.86 | 45   | 67319515540793508675715072.0000 | 0.4348   |
| 57288905682238366283726848.0000 | 14.0  | 49   | 67319515540793508675715072.0000 | 0.3696   |
| 74707823001602531749003264.0000 | 14.86 | 52   | 67319515540793508675715072.0000 | 0.3261   |
| 74707823001602531749003264.0000 | 16.0  | 56   | 67319515540793508675715072.0000 | 0.2826   |
| 74707823001602531749003264.0000 | 16.86 | 59   | 67319515540793508675715072.0000 | 0.4565   |
| 70449886504927853738459136.0000 | 18.0  | 63   | 67319515540793508675715072.0000 | 0.4348   |
| 70449886504927853738459136.0000 | 18.86 | 66   | 67319515540793508675715072.0000 | 0.4130   |
| 66191905736067400542978048.0000 | 20.0  | 70   | 67319515540793508675715072.0000 | 0.3478   |
| 66191905736067400542978048.0000 | 20.86 | 73   | 67319515540793508675715072.0000 | 0.4565   |
| 66191905736067400542978048.0000 | 22.0  | 77   | 67319515540793508675715072.0000 | 0.3478   |
| 63869401627606337277919232.0000 | 22.86 | 80   | 67319515540793508675715072.0000 | 0.4130   |
| 63869401627606337277919232.0000 | 24.0  | 84   | 67319515540793508675715072.0000 | 0.3261   |
| 63869401627606337277919232.0000 | 24.86 | 87   | 67319515540793508675715072.0000 | 0.5      |
| 63869386870211073156317184.0000 | 26.0  | 91   | 67319515540793508675715072.0000 | 0.4783   |
| 63869386870211073156317184.0000 | 26.86 | 94   | 67319515540793508675715072.0000 | 0.4565   |
| 63869386870211073156317184.0000 | 28.0  | 98   | 67319515540793508675715072.0000 | 0.4565   |
| 72385304135746204362342400.0000 | 28.86 | 101  | 67319515540793508675715072.0000 | 0.4565   |
| 72385304135746204362342400.0000 | 30.0  | 105  | 67319515540793508675715072.0000 | 0.5      |
| 72385304135746204362342400.0000 | 30.86 | 108  | 67319515540793508675715072.0000 | 0.5      |
| 65030638924441609083813888.0000 | 32.0  | 112  | 67319515540793508675715072.0000 | 0.4565   |
| 65030638924441609083813888.0000 | 32.86 | 115  | 67319515540793508675715072.0000 | 0.4565   |
| 65030638924441609083813888.0000 | 34.0  | 119  | 67319515540793508675715072.0000 | 0.4565   |
| 65998362497246039927422976.0000 | 34.29 | 120  | 67319515540793508675715072.0000 | 0.4565   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0