---
base_model: MBZUAI/swiftformer-xs
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
model-index:
- name: swiftformer-xs
  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: 0.73
    - name: Precision
      type: precision
      value: 0.5329
    - name: Recall
      type: recall
      value: 0.73
---

<!-- 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

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: 0.5838
- Accuracy: 0.73
- Precision: 0.5329
- Recall: 0.73
- F1 Score: 0.6161

## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| No log        | 1.0   | 4    | 0.6209          | 0.7292   | 0.6273    | 0.7292 | 0.6259   |
| No log        | 2.0   | 8    | 0.7514          | 0.3875   | 0.5947    | 0.3875 | 0.3910   |
| No log        | 3.0   | 12   | 0.7574          | 0.3292   | 0.6284    | 0.3292 | 0.2679   |
| 0.6558        | 4.0   | 16   | 0.7080          | 0.5042   | 0.6591    | 0.5042 | 0.5279   |
| 0.6558        | 5.0   | 20   | 0.6566          | 0.6458   | 0.6859    | 0.6458 | 0.6604   |
| 0.6558        | 6.0   | 24   | 0.6509          | 0.65     | 0.6810    | 0.65   | 0.6621   |
| 0.6558        | 7.0   | 28   | 0.6438          | 0.6375   | 0.6639    | 0.6375 | 0.6484   |
| 0.5697        | 8.0   | 32   | 0.6455          | 0.65     | 0.6845    | 0.65   | 0.6631   |
| 0.5697        | 9.0   | 36   | 0.6480          | 0.6458   | 0.6823    | 0.6458 | 0.6596   |
| 0.5697        | 10.0  | 40   | 0.6438          | 0.6542   | 0.6867    | 0.6542 | 0.6667   |
| 0.5697        | 11.0  | 44   | 0.6366          | 0.6583   | 0.6924    | 0.6583 | 0.6711   |
| 0.5232        | 12.0  | 48   | 0.6391          | 0.6625   | 0.7016    | 0.6625 | 0.6764   |
| 0.5232        | 13.0  | 52   | 0.6386          | 0.6583   | 0.6924    | 0.6583 | 0.6711   |
| 0.5232        | 14.0  | 56   | 0.6403          | 0.6667   | 0.7038    | 0.6667 | 0.68     |
| 0.5068        | 15.0  | 60   | 0.6459          | 0.6708   | 0.7131    | 0.6708 | 0.6851   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3