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# swin-tiny-patch4-window7-224-finetuned-eurosat

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: 0.1211
- Accuracy: 0.9609

## 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: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 4    | 0.4862          | 0.8516   |
| No log        | 2.0   | 8    | 0.4103          | 0.8828   |
| 0.4518        | 3.0   | 12   | 0.3210          | 0.8984   |
| 0.4518        | 4.0   | 16   | 0.2053          | 0.9375   |
| 0.2909        | 5.0   | 20   | 0.1675          | 0.9453   |
| 0.2909        | 6.0   | 24   | 0.1439          | 0.9531   |
| 0.2909        | 7.0   | 28   | 0.1448          | 0.9297   |
| 0.1492        | 8.0   | 32   | 0.1798          | 0.9531   |
| 0.1492        | 9.0   | 36   | 0.1360          | 0.9453   |
| 0.1161        | 10.0  | 40   | 0.1670          | 0.9531   |
| 0.1161        | 11.0  | 44   | 0.1637          | 0.9531   |
| 0.1161        | 12.0  | 48   | 0.1298          | 0.9531   |
| 0.1053        | 13.0  | 52   | 0.1162          | 0.9531   |
| 0.1053        | 14.0  | 56   | 0.1353          | 0.9531   |
| 0.0839        | 15.0  | 60   | 0.1211          | 0.9609   |
| 0.0839        | 16.0  | 64   | 0.1113          | 0.9609   |
| 0.0839        | 17.0  | 68   | 0.1145          | 0.9609   |
| 0.0689        | 18.0  | 72   | 0.1239          | 0.9531   |
| 0.0689        | 19.0  | 76   | 0.1280          | 0.9531   |
| 0.0581        | 20.0  | 80   | 0.1533          | 0.9531   |
| 0.0581        | 21.0  | 84   | 0.1323          | 0.9609   |
| 0.0581        | 22.0  | 88   | 0.1327          | 0.9531   |
| 0.0545        | 23.0  | 92   | 0.1529          | 0.9531   |
| 0.0545        | 24.0  | 96   | 0.1357          | 0.9531   |
| 0.046         | 25.0  | 100  | 0.1333          | 0.9531   |
| 0.046         | 26.0  | 104  | 0.1466          | 0.9531   |
| 0.046         | 27.0  | 108  | 0.1300          | 0.9531   |
| 0.0421        | 28.0  | 112  | 0.1077          | 0.9609   |
| 0.0421        | 29.0  | 116  | 0.0985          | 0.9609   |
| 0.0371        | 30.0  | 120  | 0.1186          | 0.9531   |
| 0.0371        | 31.0  | 124  | 0.1123          | 0.9531   |
| 0.0371        | 32.0  | 128  | 0.1144          | 0.9531   |
| 0.0348        | 33.0  | 132  | 0.1276          | 0.9531   |
| 0.0348        | 34.0  | 136  | 0.1488          | 0.9531   |
| 0.0211        | 35.0  | 140  | 0.1560          | 0.9531   |
| 0.0211        | 36.0  | 144  | 0.1477          | 0.9531   |
| 0.0211        | 37.0  | 148  | 0.1488          | 0.9531   |
| 0.0274        | 38.0  | 152  | 0.1467          | 0.9531   |
| 0.0274        | 39.0  | 156  | 0.1401          | 0.9531   |
| 0.0259        | 40.0  | 160  | 0.1379          | 0.9531   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "images", "split": "train", "args": "images"}, "metrics": [{"type": "accuracy", "value": 0.9609375, "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-finetuned-eurosat
==============================================


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: 0.1211
* Accuracy: 0.9609


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: 40


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### 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: 40 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* 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: 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: 40 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* 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: 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: 40### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3