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---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
model-index:
- name: swin-tiny-patch4-window7-224
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: 0.7366666666666667
- name: Precision
type: precision
value: 0.8064765100671142
- name: Recall
type: recall
value: 0.7366666666666667
---
<!-- 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. -->
# swin-tiny-patch4-window7-224
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co./microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5487
- Accuracy: 0.7367
- Precision: 0.8065
- Recall: 0.7367
- F1 Score: 0.6315
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| No log | 1.0 | 8 | 0.5835 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
| 0.6395 | 2.0 | 16 | 0.5736 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
| 0.5789 | 3.0 | 24 | 0.5943 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
| 0.5887 | 4.0 | 32 | 0.5613 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
| 0.5791 | 5.0 | 40 | 0.5557 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
| 0.5791 | 6.0 | 48 | 0.5535 | 0.7417 | 0.8090 | 0.7417 | 0.6397 |
| 0.5717 | 7.0 | 56 | 0.5456 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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