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# swin-tiny-patch4-window7-224-shortSleeveCleanedData

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: 0.0355
- Accuracy: 0.9945

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 7
- total_train_batch_size: 56
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1819        | 1.0   | 147  | 0.0471          | 0.9880   |
| 0.1431        | 2.0   | 294  | 0.0457          | 0.9891   |
| 0.1001        | 3.0   | 441  | 0.0392          | 0.9891   |
| 0.116         | 4.0   | 588  | 0.0451          | 0.9880   |
| 0.1144        | 5.0   | 735  | 0.0398          | 0.9902   |
| 0.0787        | 6.0   | 882  | 0.0441          | 0.9902   |
| 0.0998        | 7.0   | 1029 | 0.0320          | 0.9902   |
| 0.124         | 8.0   | 1176 | 0.0364          | 0.9902   |
| 0.103         | 9.0   | 1323 | 0.0395          | 0.9880   |
| 0.0591        | 10.0  | 1470 | 0.0299          | 0.9913   |
| 0.0445        | 11.0  | 1617 | 0.0302          | 0.9913   |
| 0.0684        | 12.0  | 1764 | 0.0350          | 0.9880   |
| 0.0358        | 13.0  | 1911 | 0.0408          | 0.9891   |
| 0.0548        | 14.0  | 2058 | 0.0382          | 0.9902   |
| 0.0611        | 15.0  | 2205 | 0.0331          | 0.9923   |
| 0.0231        | 16.0  | 2352 | 0.0355          | 0.9945   |
| 0.046         | 17.0  | 2499 | 0.0321          | 0.9934   |
| 0.0648        | 18.0  | 2646 | 0.0327          | 0.9923   |
| 0.0565        | 19.0  | 2793 | 0.0320          | 0.9923   |
| 0.0413        | 20.0  | 2940 | 0.0327          | 0.9923   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-shortSleeveCleanedData", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.994535519125683, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard swin image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

swin-tiny-patch4-window7-224-shortSleeveCleanedData
===================================================


This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0355
* Accuracy: 0.9945


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: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 7
* total\_train\_batch\_size: 56
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.01
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 7
* total\_train\_batch\_size: 56
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.01
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 7
* total\_train\_batch\_size: 56
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.01
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3