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---
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: swinv2-tiny-patch4-window8-256-finetuned-eurosat
results: []
---
<!-- 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. -->
# swinv2-tiny-patch4-window8-256-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6760
- Accuracy: 0.8170
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.2519 | 0.9955 | 167 | 0.7302 | 0.8017 |
| 0.2407 | 1.9970 | 335 | 0.7095 | 0.7836 |
| 0.3423 | 2.9985 | 503 | 0.7016 | 0.7884 |
| 0.4687 | 4.0 | 671 | 0.6480 | 0.7969 |
| 0.4789 | 4.9955 | 838 | 0.5132 | 0.8160 |
| 0.4417 | 5.9970 | 1006 | 0.5321 | 0.8065 |
| 0.435 | 6.9985 | 1174 | 0.5770 | 0.8093 |
| 0.4106 | 8.0 | 1342 | 0.5650 | 0.8189 |
| 0.4216 | 8.9955 | 1509 | 0.5535 | 0.8132 |
| 0.3786 | 9.9970 | 1677 | 0.5745 | 0.8179 |
| 0.3536 | 10.9985 | 1845 | 0.6322 | 0.8046 |
| 0.4842 | 12.0 | 2013 | 0.7200 | 0.8103 |
| 0.3095 | 12.9955 | 2180 | 0.6996 | 0.8112 |
| 0.2603 | 13.9970 | 2348 | 0.7004 | 0.8065 |
| 0.2838 | 14.9985 | 2516 | 0.6331 | 0.8227 |
| 0.3449 | 16.0 | 2684 | 0.6788 | 0.8122 |
| 0.253 | 16.9955 | 2851 | 0.6940 | 0.8103 |
| 0.2647 | 17.9970 | 3019 | 0.6770 | 0.8132 |
| 0.2991 | 18.9985 | 3187 | 0.6647 | 0.8189 |
| 0.26 | 19.9106 | 3340 | 0.6760 | 0.8170 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.2
- Datasets 2.19.1
- Tokenizers 0.19.1
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