---
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: 0.7391304347826086
---

<!-- 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: 0.8596
- Accuracy: 0.7391

## 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: 0.0015
- 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    | 1.3836          | 0.4565   |
| No log        | 2.0   | 7    | 1.3327          | 0.6522   |
| 1.3567        | 2.86  | 10   | 1.1681          | 0.6522   |
| 1.3567        | 4.0   | 14   | 1.0440          | 0.5652   |
| 1.3567        | 4.86  | 17   | 1.0462          | 0.6304   |
| 1.0903        | 6.0   | 21   | 0.9294          | 0.5870   |
| 1.0903        | 6.86  | 24   | 0.9572          | 0.6522   |
| 1.0903        | 8.0   | 28   | 0.9286          | 0.6739   |
| 1.0969        | 8.86  | 31   | 0.9229          | 0.6304   |
| 1.0969        | 10.0  | 35   | 0.9061          | 0.6522   |
| 1.0969        | 10.86 | 38   | 0.8341          | 0.6739   |
| 0.8923        | 12.0  | 42   | 0.8786          | 0.6739   |
| 0.8923        | 12.86 | 45   | 0.8596          | 0.7391   |
| 0.8923        | 14.0  | 49   | 0.8902          | 0.7174   |
| 0.7289        | 14.86 | 52   | 0.8024          | 0.6739   |
| 0.7289        | 16.0  | 56   | 0.9341          | 0.7174   |
| 0.7289        | 16.86 | 59   | 1.0464          | 0.7174   |
| 0.6609        | 18.0  | 63   | 0.9923          | 0.6087   |
| 0.6609        | 18.86 | 66   | 0.8225          | 0.7174   |
| 0.6527        | 20.0  | 70   | 0.8748          | 0.6957   |
| 0.6527        | 20.86 | 73   | 0.8052          | 0.6739   |
| 0.6527        | 22.0  | 77   | 0.8861          | 0.6957   |
| 0.493         | 22.86 | 80   | 0.9555          | 0.6957   |
| 0.493         | 24.0  | 84   | 1.0336          | 0.6739   |
| 0.493         | 24.86 | 87   | 0.9961          | 0.6957   |
| 0.4088        | 26.0  | 91   | 1.0400          | 0.6957   |
| 0.4088        | 26.86 | 94   | 1.0536          | 0.6957   |
| 0.4088        | 28.0  | 98   | 1.1388          | 0.6739   |
| 0.4047        | 28.86 | 101  | 1.2295          | 0.6522   |
| 0.4047        | 30.0  | 105  | 1.2627          | 0.6522   |
| 0.4047        | 30.86 | 108  | 1.2372          | 0.6739   |
| 0.3681        | 32.0  | 112  | 1.2919          | 0.6522   |
| 0.3681        | 32.86 | 115  | 1.2453          | 0.6522   |
| 0.3681        | 34.0  | 119  | 1.2612          | 0.6739   |
| 0.353         | 34.29 | 120  | 1.2611          | 0.6957   |


### Framework versions

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