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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: 0.3266
- Accuracy: 0.8465

## 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2941        | 1.0   | 17   | 1.1717          | 0.4689   |
| 1.0655        | 2.0   | 34   | 0.9397          | 0.5560   |
| 0.8008        | 3.0   | 51   | 0.6153          | 0.7303   |
| 0.7204        | 4.0   | 68   | 0.5665          | 0.7427   |
| 0.6931        | 5.0   | 85   | 0.4670          | 0.7801   |
| 0.6277        | 6.0   | 102  | 0.4328          | 0.8465   |
| 0.5689        | 7.0   | 119  | 0.4078          | 0.8174   |
| 0.6103        | 8.0   | 136  | 0.4060          | 0.8091   |
| 0.5501        | 9.0   | 153  | 0.4842          | 0.7884   |
| 0.6018        | 10.0  | 170  | 0.3780          | 0.8423   |
| 0.5668        | 11.0  | 187  | 0.3551          | 0.8631   |
| 0.5192        | 12.0  | 204  | 0.4514          | 0.8216   |
| 0.5133        | 13.0  | 221  | 0.3598          | 0.8174   |
| 0.5753        | 14.0  | 238  | 0.4172          | 0.8091   |
| 0.4833        | 15.0  | 255  | 0.4685          | 0.8050   |
| 0.5546        | 16.0  | 272  | 0.4474          | 0.7842   |
| 0.5179        | 17.0  | 289  | 0.4570          | 0.7884   |
| 0.5017        | 18.0  | 306  | 0.4218          | 0.8050   |
| 0.4808        | 19.0  | 323  | 0.4094          | 0.8050   |
| 0.4708        | 20.0  | 340  | 0.4693          | 0.7759   |
| 0.5033        | 21.0  | 357  | 0.3141          | 0.8672   |
| 0.4859        | 22.0  | 374  | 0.3687          | 0.8257   |
| 0.516         | 23.0  | 391  | 0.3819          | 0.8216   |
| 0.4822        | 24.0  | 408  | 0.3391          | 0.8506   |
| 0.4748        | 25.0  | 425  | 0.3281          | 0.8506   |
| 0.4914        | 26.0  | 442  | 0.3308          | 0.8631   |
| 0.4354        | 27.0  | 459  | 0.3859          | 0.8133   |
| 0.4297        | 28.0  | 476  | 0.3761          | 0.8133   |
| 0.4747        | 29.0  | 493  | 0.2914          | 0.8672   |
| 0.4395        | 30.0  | 510  | 0.3025          | 0.8548   |
| 0.4279        | 31.0  | 527  | 0.3314          | 0.8506   |
| 0.4327        | 32.0  | 544  | 0.4626          | 0.7842   |
| 0.446         | 33.0  | 561  | 0.3499          | 0.8382   |
| 0.4011        | 34.0  | 578  | 0.3408          | 0.8465   |
| 0.4418        | 35.0  | 595  | 0.3159          | 0.8589   |
| 0.484         | 36.0  | 612  | 0.3130          | 0.8548   |
| 0.4119        | 37.0  | 629  | 0.2899          | 0.8589   |
| 0.4453        | 38.0  | 646  | 0.3200          | 0.8465   |
| 0.4074        | 39.0  | 663  | 0.3493          | 0.8465   |
| 0.3937        | 40.0  | 680  | 0.3003          | 0.8672   |
| 0.4222        | 41.0  | 697  | 0.3547          | 0.8299   |
| 0.3922        | 42.0  | 714  | 0.3206          | 0.8589   |
| 0.3973        | 43.0  | 731  | 0.4074          | 0.8133   |
| 0.4118        | 44.0  | 748  | 0.3147          | 0.8589   |
| 0.4088        | 45.0  | 765  | 0.3393          | 0.8506   |
| 0.3635        | 46.0  | 782  | 0.3584          | 0.8257   |
| 0.403         | 47.0  | 799  | 0.3240          | 0.8506   |
| 0.3943        | 48.0  | 816  | 0.3536          | 0.8216   |
| 0.4085        | 49.0  | 833  | 0.3270          | 0.8465   |
| 0.3865        | 50.0  | 850  | 0.3266          | 0.8465   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1

{"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", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8464730290456431, "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: 0.3266
* Accuracy: 0.8465


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: 50


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1



### 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: 50 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
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: 50 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: 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: 50### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1