transformers safetensors swin image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/swin-tiny-patch4-window7-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9675324675324676
---

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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: 0.1073
- Accuracy: 0.9675

## 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 5    | 0.6616          | 0.6299   |
| 0.6583        | 2.0   | 10   | 0.5232          | 0.7597   |
| 0.6583        | 3.0   | 15   | 0.5043          | 0.7857   |
| 0.3346        | 4.0   | 20   | 0.2879          | 0.8766   |
| 0.3346        | 5.0   | 25   | 0.2424          | 0.9091   |
| 0.1544        | 6.0   | 30   | 0.2217          | 0.8896   |
| 0.1544        | 7.0   | 35   | 0.1466          | 0.9221   |
| 0.088         | 8.0   | 40   | 0.1261          | 0.9481   |
| 0.088         | 9.0   | 45   | 0.1680          | 0.9221   |
| 0.0977        | 10.0  | 50   | 0.1446          | 0.9351   |
| 0.0977        | 11.0  | 55   | 0.1812          | 0.9221   |
| 0.0719        | 12.0  | 60   | 0.1798          | 0.9286   |
| 0.0719        | 13.0  | 65   | 0.1056          | 0.9610   |
| 0.0629        | 14.0  | 70   | 0.1073          | 0.9675   |
| 0.0629        | 15.0  | 75   | 0.1106          | 0.9545   |
| 0.0414        | 16.0  | 80   | 0.1286          | 0.9416   |
| 0.0414        | 17.0  | 85   | 0.0761          | 0.9610   |
| 0.0397        | 18.0  | 90   | 0.0785          | 0.9675   |
| 0.0397        | 19.0  | 95   | 0.0746          | 0.9675   |
| 0.0487        | 20.0  | 100  | 0.0684          | 0.9675   |
| 0.0487        | 21.0  | 105  | 0.0602          | 0.9610   |
| 0.0244        | 22.0  | 110  | 0.0551          | 0.9675   |
| 0.0244        | 23.0  | 115  | 0.0639          | 0.9675   |
| 0.0214        | 24.0  | 120  | 0.0583          | 0.9675   |
| 0.0214        | 25.0  | 125  | 0.0663          | 0.9675   |
| 0.0261        | 26.0  | 130  | 0.1006          | 0.9610   |
| 0.0261        | 27.0  | 135  | 0.0711          | 0.9675   |
| 0.019         | 28.0  | 140  | 0.0629          | 0.9675   |
| 0.019         | 29.0  | 145  | 0.0728          | 0.9610   |
| 0.0237        | 30.0  | 150  | 0.0747          | 0.9610   |


### Framework versions

- Transformers 4.38.1
- Pytorch 1.10.0+cu111
- Datasets 2.17.1
- Tokenizers 0.15.2
