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swiftformer-xs-DMAE-ALT

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swiftformer-xs-DMAE-ALT
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---

base_model: MBZUAI/swiftformer-xs
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swiftformer-xs-DMAE-ALT
  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: 0.6521739130434783
---


<!-- 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-ALT

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: 6162013035452755345408.0000
- Accuracy: 0.6522

## 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: 1.5e-05

- 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: 0.01

- num_epochs: 40

### Training results

| Training Loss               | Epoch | Step | Validation Loss             | Accuracy |
|:---------------------------:|:-----:|:----:|:---------------------------:|:--------:|
| No log                      | 0.86  | 3    | 6162013035452755345408.0000 | 0.4348   |
| No log                      | 2.0   | 7    | 6162013035452755345408.0000 | 0.5217   |
| 6041083954518472785920.0000 | 2.86  | 10   | 6162013035452755345408.0000 | 0.6304   |
| 6041083954518472785920.0000 | 4.0   | 14   | 6162013035452755345408.0000 | 0.6304   |
| 6041083954518472785920.0000 | 4.86  | 17   | 6162013035452755345408.0000 | 0.6087   |
| 6324536912185469698048.0000 | 6.0   | 21   | 6162013035452755345408.0000 | 0.6087   |
| 6324536912185469698048.0000 | 6.86  | 24   | 6162013035452755345408.0000 | 0.5870   |
| 6324536912185469698048.0000 | 8.0   | 28   | 6162013035452755345408.0000 | 0.5870   |
| 7104031645049785679872.0000 | 8.86  | 31   | 6162013035452755345408.0000 | 0.6304   |
| 7104031645049785679872.0000 | 10.0  | 35   | 6162013035452755345408.0000 | 0.6304   |
| 7104031645049785679872.0000 | 10.86 | 38   | 6162013035452755345408.0000 | 0.6304   |
| 5243873411799968645120.0000 | 12.0  | 42   | 6162013035452755345408.0000 | 0.6087   |
| 5243873411799968645120.0000 | 12.86 | 45   | 6162013035452755345408.0000 | 0.6087   |
| 5243873411799968645120.0000 | 14.0  | 49   | 6162013035452755345408.0000 | 0.6304   |
| 6838294497236975878144.0000 | 14.86 | 52   | 6162013035452755345408.0000 | 0.6304   |
| 6838294497236975878144.0000 | 16.0  | 56   | 6162013035452755345408.0000 | 0.6304   |
| 6838294497236975878144.0000 | 16.86 | 59   | 6162013035452755345408.0000 | 0.6304   |
| 6448545779724929990656.0000 | 18.0  | 63   | 6162013035452755345408.0000 | 0.6304   |
| 6448545779724929990656.0000 | 18.86 | 66   | 6162013035452755345408.0000 | 0.6304   |
| 6058800665092585160704.0000 | 20.0  | 70   | 6162013035452755345408.0000 | 0.6304   |
| 6058800665092585160704.0000 | 20.86 | 73   | 6162013035452755345408.0000 | 0.6304   |
| 6058800665092585160704.0000 | 22.0  | 77   | 6162013035452755345408.0000 | 0.6304   |
| 5846209595762449317888.0000 | 22.86 | 80   | 6162013035452755345408.0000 | 0.6304   |
| 5846209595762449317888.0000 | 24.0  | 84   | 6162013035452755345408.0000 | 0.6304   |
| 5846209595762449317888.0000 | 24.86 | 87   | 6162013035452755345408.0000 | 0.6522   |
| 5846210496482374582272.0000 | 26.0  | 91   | 6162013035452755345408.0000 | 0.6304   |
| 5846210496482374582272.0000 | 26.86 | 94   | 6162013035452755345408.0000 | 0.6304   |
| 5846210496482374582272.0000 | 28.0  | 98   | 6162013035452755345408.0000 | 0.6522   |
| 6625704778986728456192.0000 | 28.86 | 101  | 6162013035452755345408.0000 | 0.6304   |
| 6625704778986728456192.0000 | 30.0  | 105  | 6162013035452755345408.0000 | 0.6304   |
| 6625704778986728456192.0000 | 30.86 | 108  | 6162013035452755345408.0000 | 0.6522   |
| 5952505355607498293248.0000 | 32.0  | 112  | 6162013035452755345408.0000 | 0.6304   |
| 5952505355607498293248.0000 | 32.86 | 115  | 6162013035452755345408.0000 | 0.6304   |
| 5952505355607498293248.0000 | 34.0  | 119  | 6162013035452755345408.0000 | 0.6304   |
| 6041083504158509629440.0000 | 34.29 | 120  | 6162013035452755345408.0000 | 0.6304   |


### Framework versions

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