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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
- nielsr/eurosat-demo
metrics:
- accuracy
widget:
- src: https://drive.google.com/uc?id=1trKgvkMRQ3BB0VcqnDwmieLxXhWmS8rq
example_title: Annual Crop
- src: https://drive.google.com/uc?id=1kWQbPNHVa_JscS0age5E0UOSBcU1bh18
example_title: Forest
- src: https://drive.google.com/uc?id=12YbxF-MfpMqLPB91HuTPEgcg1xnZKhGP
example_title: Herbaceous Vegetation
- src: https://drive.google.com/uc?id=1NkzDiaQ1ciMDf89C8uA5zGx984bwkFCi
example_title: Highway
- src: https://drive.google.com/uc?id=1F6r7O0rlgzaPvY6XBpFOWUTIddEIUkxx
example_title: Industrial
- src: https://drive.google.com/uc?id=16zOtFHZ9E17jA9Ua4PsXrUjugSs77XKm
example_title: Pasture
- src: https://drive.google.com/uc?id=163tqIdoVY7WFtKQlpz_bPM9WjwbJAtd
example_title: Permanent Crop
- src: https://drive.google.com/uc?id=1qsX-XsrE3dMp7C7LLVa6HriaABIXuBrJ
example_title: Residential
- src: https://drive.google.com/uc?id=1UK2praQHbNXDnctJt58rrlQZu84lxyk
example_title: River
- src: https://drive.google.com/uc?id=1zVAfR7N5hXy6eq1cVOd8bXPjC1sqxVir
example_title: Sea Lake
base_model: microsoft/swin-tiny-patch4-window7-224
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
type: image-classification
name: Image Classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- type: accuracy
value: 0.9848148148148148
name: Accuracy
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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 image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0536
- Accuracy: 0.9848
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2602 | 1.0 | 190 | 0.1310 | 0.9563 |
| 0.1975 | 2.0 | 380 | 0.1063 | 0.9637 |
| 0.142 | 3.0 | 570 | 0.0642 | 0.9767 |
| 0.1235 | 4.0 | 760 | 0.0560 | 0.9837 |
| 0.1019 | 5.0 | 950 | 0.0536 | 0.9848 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|