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---
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: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.805
---
<!-- 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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6650
- Accuracy: 0.805
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.2566 | 0.9825 | 14 | 0.7227 | 0.74 |
| 0.2729 | 1.9649 | 28 | 0.3916 | 0.815 |
| 0.1553 | 2.9474 | 42 | 0.8409 | 0.75 |
| 0.1371 | 4.0 | 57 | 0.3706 | 0.885 |
| 0.1588 | 4.9825 | 71 | 0.8758 | 0.765 |
| 0.1093 | 5.9649 | 85 | 0.6063 | 0.82 |
| 0.102 | 6.9474 | 99 | 0.5308 | 0.84 |
| 0.0713 | 8.0 | 114 | 0.3536 | 0.88 |
| 0.1045 | 8.9825 | 128 | 0.3858 | 0.875 |
| 0.0661 | 9.8246 | 140 | 0.6650 | 0.805 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|