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

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7576
- Accuracy: 0.5862

## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6968        | 0.98  | 32   | 0.7576          | 0.5862   |
| 0.6144        | 2.0   | 65   | 0.7457          | 0.5862   |
| 0.5981        | 2.95  | 96   | 0.6591          | 0.5862   |


### Framework versions

- Transformers 4.29.2
- Pytorch 1.12.1
- Datasets 2.12.0
- Tokenizers 0.13.3

{"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": 0.5862068965517241, "name": "Accuracy"}]}]}]}
transformers pytorch swin image-classification generated_from_trainer dataset:imagefolder model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #swin #image-classification #generated_from_trainer #dataset-imagefolder #model-index #autotrain_compatible #endpoints_compatible #region-us 

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


This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7576
* Accuracy: 0.5862


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: 0.0001
* train\_batch\_size: 1
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 1.12.1
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 1
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 1.12.1
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #swin #image-classification #generated_from_trainer #dataset-imagefolder #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 1
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 1.12.1
* Datasets 2.12.0
* Tokenizers 0.13.3