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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: 1.0291
- Accuracy: 0.5714

## 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: 90

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 1    | 0.9799          | 0.4286   |
| No log        | 2.0   | 2    | 0.9703          | 0.4286   |
| No log        | 3.0   | 3    | 0.9703          | 0.4286   |
| No log        | 4.0   | 4    | 0.9699          | 0.4286   |
| No log        | 5.0   | 5    | 0.9699          | 0.4286   |
| No log        | 6.0   | 6    | 0.9881          | 0.4286   |
| No log        | 7.0   | 7    | 0.9881          | 0.4286   |
| No log        | 8.0   | 8    | 1.0213          | 0.4286   |
| No log        | 9.0   | 9    | 1.0213          | 0.4286   |
| 0.426         | 10.0  | 10   | 1.0291          | 0.5714   |
| 0.426         | 11.0  | 11   | 1.0291          | 0.5714   |
| 0.426         | 12.0  | 12   | 0.9996          | 0.5714   |
| 0.426         | 13.0  | 13   | 0.9996          | 0.5714   |
| 0.426         | 14.0  | 14   | 0.8998          | 0.5714   |
| 0.426         | 15.0  | 15   | 0.8998          | 0.5714   |
| 0.426         | 16.0  | 16   | 0.8356          | 0.5714   |
| 0.426         | 17.0  | 17   | 0.8356          | 0.5714   |
| 0.426         | 18.0  | 18   | 0.8575          | 0.5714   |
| 0.426         | 19.0  | 19   | 0.8575          | 0.5714   |
| 0.324         | 20.0  | 20   | 0.9310          | 0.4286   |
| 0.324         | 21.0  | 21   | 0.9310          | 0.4286   |
| 0.324         | 22.0  | 22   | 1.0029          | 0.4286   |
| 0.324         | 23.0  | 23   | 1.0029          | 0.4286   |
| 0.324         | 24.0  | 24   | 1.0582          | 0.4286   |
| 0.324         | 25.0  | 25   | 1.0582          | 0.4286   |
| 0.324         | 26.0  | 26   | 1.0812          | 0.4286   |
| 0.324         | 27.0  | 27   | 1.0812          | 0.4286   |
| 0.324         | 28.0  | 28   | 1.0345          | 0.4286   |
| 0.324         | 29.0  | 29   | 1.0345          | 0.4286   |
| 0.2536        | 30.0  | 30   | 0.9996          | 0.4286   |
| 0.2536        | 31.0  | 31   | 0.9996          | 0.4286   |
| 0.2536        | 32.0  | 32   | 0.9401          | 0.5714   |
| 0.2536        | 33.0  | 33   | 0.9401          | 0.5714   |
| 0.2536        | 34.0  | 34   | 0.8978          | 0.5714   |
| 0.2536        | 35.0  | 35   | 0.8978          | 0.5714   |
| 0.2536        | 36.0  | 36   | 0.9056          | 0.5714   |
| 0.2536        | 37.0  | 37   | 0.9056          | 0.5714   |
| 0.2536        | 38.0  | 38   | 0.9364          | 0.5714   |
| 0.2536        | 39.0  | 39   | 0.9364          | 0.5714   |
| 0.2176        | 40.0  | 40   | 1.0523          | 0.5714   |
| 0.2176        | 41.0  | 41   | 1.0523          | 0.5714   |
| 0.2176        | 42.0  | 42   | 1.1687          | 0.4286   |
| 0.2176        | 43.0  | 43   | 1.1687          | 0.4286   |
| 0.2176        | 44.0  | 44   | 1.1968          | 0.4286   |
| 0.2176        | 45.0  | 45   | 1.1968          | 0.4286   |
| 0.2176        | 46.0  | 46   | 1.1604          | 0.4286   |
| 0.2176        | 47.0  | 47   | 1.1604          | 0.4286   |
| 0.2176        | 48.0  | 48   | 1.0505          | 0.4286   |
| 0.2176        | 49.0  | 49   | 1.0505          | 0.4286   |
| 0.1597        | 50.0  | 50   | 0.9059          | 0.5714   |
| 0.1597        | 51.0  | 51   | 0.9059          | 0.5714   |
| 0.1597        | 52.0  | 52   | 0.8606          | 0.5714   |
| 0.1597        | 53.0  | 53   | 0.8606          | 0.5714   |
| 0.1597        | 54.0  | 54   | 0.8946          | 0.5714   |
| 0.1597        | 55.0  | 55   | 0.8946          | 0.5714   |
| 0.1597        | 56.0  | 56   | 0.9643          | 0.5714   |
| 0.1597        | 57.0  | 57   | 0.9643          | 0.5714   |
| 0.1597        | 58.0  | 58   | 1.0598          | 0.5714   |
| 0.1597        | 59.0  | 59   | 1.0598          | 0.5714   |
| 0.1231        | 60.0  | 60   | 1.1833          | 0.5714   |
| 0.1231        | 61.0  | 61   | 1.1833          | 0.5714   |
| 0.1231        | 62.0  | 62   | 1.2730          | 0.5714   |
| 0.1231        | 63.0  | 63   | 1.2730          | 0.5714   |
| 0.1231        | 64.0  | 64   | 1.3132          | 0.4286   |
| 0.1231        | 65.0  | 65   | 1.3132          | 0.4286   |
| 0.1231        | 66.0  | 66   | 1.3025          | 0.4286   |
| 0.1231        | 67.0  | 67   | 1.3025          | 0.4286   |
| 0.1231        | 68.0  | 68   | 1.2702          | 0.4286   |
| 0.1231        | 69.0  | 69   | 1.2702          | 0.4286   |
| 0.1364        | 70.0  | 70   | 1.2411          | 0.4286   |
| 0.1364        | 71.0  | 71   | 1.2411          | 0.4286   |
| 0.1364        | 72.0  | 72   | 1.2222          | 0.4286   |
| 0.1364        | 73.0  | 73   | 1.2222          | 0.4286   |
| 0.1364        | 74.0  | 74   | 1.2257          | 0.4286   |
| 0.1364        | 75.0  | 75   | 1.2257          | 0.4286   |
| 0.1364        | 76.0  | 76   | 1.2552          | 0.4286   |
| 0.1364        | 77.0  | 77   | 1.2552          | 0.4286   |
| 0.1364        | 78.0  | 78   | 1.2701          | 0.5714   |
| 0.1364        | 79.0  | 79   | 1.2701          | 0.5714   |
| 0.0937        | 80.0  | 80   | 1.2753          | 0.5714   |
| 0.0937        | 81.0  | 81   | 1.2753          | 0.5714   |
| 0.0937        | 82.0  | 82   | 1.2797          | 0.5714   |
| 0.0937        | 83.0  | 83   | 1.2797          | 0.5714   |
| 0.0937        | 84.0  | 84   | 1.2840          | 0.5714   |
| 0.0937        | 85.0  | 85   | 1.2840          | 0.5714   |
| 0.0937        | 86.0  | 86   | 1.2895          | 0.5714   |
| 0.0937        | 87.0  | 87   | 1.2895          | 0.5714   |
| 0.0937        | 88.0  | 88   | 1.2917          | 0.5714   |
| 0.0937        | 89.0  | 89   | 1.2917          | 0.5714   |
| 0.1082        | 90.0  | 90   | 1.2931          | 0.5714   |


### Framework versions

- Transformers 4.30.1
- Pytorch 2.0.1+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-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.5714285714285714, "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: 1.0291
* Accuracy: 0.5714


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: 90


### Training results



### Framework versions


* Transformers 4.30.1
* Pytorch 2.0.1+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: 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: 90 ### Training results ### Framework versions


* Transformers 4.30.1
* Pytorch 2.0.1+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: 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: 90 ### Training results ### Framework versions


* Transformers 4.30.1
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3