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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: 0.8033333333333333
- name: Precision
type: precision
value: 0.7970708748615725
- name: Recall
type: recall
value: 0.8033333333333333
---
<!-- 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: 0.4788
- Accuracy: 0.8033
- Precision: 0.7971
- Recall: 0.8033
- F1 Score: 0.7802
## 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 | 0.5946 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
| No log | 2.0 | 8 | 0.6006 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
| No log | 3.0 | 12 | 0.5677 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
| No log | 4.0 | 16 | 0.5616 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
| No log | 5.0 | 20 | 0.5556 | 0.75 | 0.7193 | 0.75 | 0.7023 |
| No log | 6.0 | 24 | 0.5435 | 0.7667 | 0.7819 | 0.7667 | 0.7019 |
| No log | 7.0 | 28 | 0.5318 | 0.7792 | 0.7885 | 0.7792 | 0.7281 |
| 0.5745 | 8.0 | 32 | 0.5316 | 0.7542 | 0.7262 | 0.7542 | 0.7126 |
| 0.5745 | 9.0 | 36 | 0.5232 | 0.7667 | 0.7533 | 0.7667 | 0.7185 |
| 0.5745 | 10.0 | 40 | 0.5226 | 0.7708 | 0.7639 | 0.7708 | 0.7217 |
| 0.5745 | 11.0 | 44 | 0.5217 | 0.7708 | 0.7597 | 0.7708 | 0.7253 |
| 0.5745 | 12.0 | 48 | 0.5224 | 0.7625 | 0.7561 | 0.7625 | 0.7034 |
| 0.5745 | 13.0 | 52 | 0.5213 | 0.7708 | 0.7510 | 0.7708 | 0.7409 |
| 0.5745 | 14.0 | 56 | 0.5207 | 0.7667 | 0.7709 | 0.7667 | 0.7064 |
| 0.4741 | 15.0 | 60 | 0.5247 | 0.7583 | 0.7343 | 0.7583 | 0.7334 |
| 0.4741 | 16.0 | 64 | 0.5352 | 0.7708 | 0.7639 | 0.7708 | 0.7217 |
| 0.4741 | 17.0 | 68 | 0.5227 | 0.7708 | 0.7507 | 0.7708 | 0.7460 |
| 0.4741 | 18.0 | 72 | 0.5206 | 0.7583 | 0.7564 | 0.7583 | 0.6912 |
| 0.4741 | 19.0 | 76 | 0.5088 | 0.775 | 0.7627 | 0.775 | 0.7353 |
| 0.4741 | 20.0 | 80 | 0.5144 | 0.7667 | 0.7503 | 0.7667 | 0.7221 |
| 0.4741 | 21.0 | 84 | 0.5227 | 0.7875 | 0.7918 | 0.7875 | 0.7453 |
| 0.4741 | 22.0 | 88 | 0.5150 | 0.775 | 0.7564 | 0.775 | 0.7494 |
| 0.4233 | 23.0 | 92 | 0.5240 | 0.7667 | 0.7533 | 0.7667 | 0.7185 |
| 0.4233 | 24.0 | 96 | 0.5156 | 0.7792 | 0.7684 | 0.7792 | 0.7418 |
| 0.4233 | 25.0 | 100 | 0.5141 | 0.7792 | 0.7631 | 0.7792 | 0.7503 |
| 0.4233 | 26.0 | 104 | 0.5234 | 0.7833 | 0.7813 | 0.7833 | 0.7420 |
| 0.4233 | 27.0 | 108 | 0.5175 | 0.7833 | 0.7813 | 0.7833 | 0.7420 |
| 0.4233 | 28.0 | 112 | 0.5122 | 0.7958 | 0.7856 | 0.7958 | 0.7715 |
| 0.4233 | 29.0 | 116 | 0.5126 | 0.7958 | 0.7856 | 0.7958 | 0.7715 |
| 0.3931 | 30.0 | 120 | 0.5130 | 0.7958 | 0.7856 | 0.7958 | 0.7715 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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