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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: 0.73
- 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
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