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---
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: 0.57
- name: Precision
type: precision
value: 0.59945
- name: Recall
type: recall
value: 0.57
---
<!-- 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: 0.6833
- Accuracy: 0.57
- Precision: 0.5995
- Recall: 0.57
- F1 Score: 0.5828
## 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: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| No log | 1.0 | 4 | 0.6713 | 0.6292 | 0.6454 | 0.6292 | 0.6365 |
| No log | 2.0 | 8 | 0.7142 | 0.475 | 0.6155 | 0.475 | 0.5020 |
| No log | 3.0 | 12 | 0.7298 | 0.425 | 0.6026 | 0.425 | 0.4435 |
| No log | 4.0 | 16 | 0.7389 | 0.4792 | 0.6408 | 0.4792 | 0.5023 |
| No log | 5.0 | 20 | 0.7427 | 0.4792 | 0.6408 | 0.4792 | 0.5023 |
| No log | 6.0 | 24 | 0.7235 | 0.5083 | 0.6424 | 0.5083 | 0.5348 |
| No log | 7.0 | 28 | 0.6893 | 0.5875 | 0.6687 | 0.5875 | 0.6107 |
| 0.6981 | 8.0 | 32 | 0.6816 | 0.6042 | 0.6847 | 0.6042 | 0.6264 |
| 0.6981 | 9.0 | 36 | 0.6866 | 0.6042 | 0.6888 | 0.6042 | 0.6266 |
| 0.6981 | 10.0 | 40 | 0.7005 | 0.575 | 0.6751 | 0.575 | 0.5996 |
| 0.6981 | 11.0 | 44 | 0.7127 | 0.525 | 0.6554 | 0.525 | 0.5510 |
| 0.6981 | 12.0 | 48 | 0.7098 | 0.5333 | 0.6595 | 0.5333 | 0.5593 |
| 0.6981 | 13.0 | 52 | 0.7126 | 0.5208 | 0.6579 | 0.5208 | 0.5463 |
| 0.6981 | 14.0 | 56 | 0.7114 | 0.5292 | 0.6575 | 0.5292 | 0.5551 |
| 0.6656 | 15.0 | 60 | 0.6908 | 0.5667 | 0.6712 | 0.5667 | 0.5917 |
| 0.6656 | 16.0 | 64 | 0.6804 | 0.5833 | 0.6749 | 0.5833 | 0.6073 |
| 0.6656 | 17.0 | 68 | 0.6806 | 0.5958 | 0.6808 | 0.5958 | 0.6188 |
| 0.6656 | 18.0 | 72 | 0.6884 | 0.5583 | 0.6629 | 0.5583 | 0.5838 |
| 0.6656 | 19.0 | 76 | 0.6821 | 0.5708 | 0.6647 | 0.5708 | 0.5955 |
| 0.6656 | 20.0 | 80 | 0.6663 | 0.6042 | 0.6806 | 0.6042 | 0.6261 |
| 0.6656 | 21.0 | 84 | 0.6717 | 0.6 | 0.6787 | 0.6 | 0.6223 |
| 0.6656 | 22.0 | 88 | 0.6682 | 0.6083 | 0.6826 | 0.6083 | 0.6299 |
| 0.6443 | 23.0 | 92 | 0.6683 | 0.6167 | 0.6946 | 0.6167 | 0.6381 |
| 0.6443 | 24.0 | 96 | 0.6733 | 0.6 | 0.6911 | 0.6 | 0.6230 |
| 0.6443 | 25.0 | 100 | 0.6647 | 0.6083 | 0.6866 | 0.6083 | 0.6302 |
| 0.6443 | 26.0 | 104 | 0.6729 | 0.6083 | 0.6907 | 0.6083 | 0.6305 |
| 0.6443 | 27.0 | 108 | 0.6740 | 0.6042 | 0.6930 | 0.6042 | 0.6268 |
| 0.6443 | 28.0 | 112 | 0.6809 | 0.5917 | 0.6916 | 0.5917 | 0.6153 |
| 0.6443 | 29.0 | 116 | 0.6778 | 0.6042 | 0.7017 | 0.6042 | 0.6270 |
| 0.6313 | 30.0 | 120 | 0.6794 | 0.5958 | 0.6935 | 0.5958 | 0.6192 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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