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swin-tiny-patch4-window7-224-finetuned-eurosat
navradio/swin-tiny-patch4-window7-224-finetuned-eurosat
swin-tiny-patch4-window7-224-finetuned-eurosat is a image classification model from navradio. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- swin
- Access
- Public
- Created
- Jun 28, 2023
- Updated
- Sep 29, 2023
- SHA
- 557e9a7f
Base models
Datasets
Model card
From the Hugging Face model README
<!-- 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 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 5.5031
- Accuracy: 0.0
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: 256
- eval_batch_size: 256
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 1024
- 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 | 1 | 0.6744 | 1.0 |
| No log | 2.0 | 2 | 0.7507 | 0.0 |
| No log | 3.0 | 3 | 0.9175 | 0.0 |
| No log | 4.0 | 4 | 1.1669 | 0.0 |
| No log | 5.0 | 5 | 1.4443 | 0.0 |
| No log | 6.0 | 6 | 1.7218 | 0.0 |
| No log | 7.0 | 7 | 2.0269 | 0.0 |
| No log | 8.0 | 8 | 2.3374 | 0.0 |
| No log | 9.0 | 9 | 2.6657 | 0.0 |
| 0.0781 | 10.0 | 10 | 2.9900 | 0.0 |
| 0.0781 | 11.0 | 11 | 3.2990 | 0.0 |
| 0.0781 | 12.0 | 12 | 3.5921 | 0.0 |
| 0.0781 | 13.0 | 13 | 3.8577 | 0.0 |
| 0.0781 | 14.0 | 14 | 4.1048 | 0.0 |
| 0.0781 | 15.0 | 15 | 4.3232 | 0.0 |
| 0.0781 | 16.0 | 16 | 4.5163 | 0.0 |
| 0.0781 | 17.0 | 17 | 4.6854 | 0.0 |
| 0.0781 | 18.0 | 18 | 4.8332 | 0.0 |
| 0.0781 | 19.0 | 19 | 4.9602 | 0.0 |
| 0.0003 | 20.0 | 20 | 5.0735 | 0.0 |
| 0.0003 | 21.0 | 21 | 5.1691 | 0.0 |
| 0.0003 | 22.0 | 22 | 5.2486 | 0.0 |
| 0.0003 | 23.0 | 23 | 5.3151 | 0.0 |
| 0.0003 | 24.0 | 24 | 5.3696 | 0.0 |
| 0.0003 | 25.0 | 25 | 5.4131 | 0.0 |
| 0.0003 | 26.0 | 26 | 5.4466 | 0.0 |
| 0.0003 | 27.0 | 27 | 5.4711 | 0.0 |
| 0.0003 | 28.0 | 28 | 5.4879 | 0.0 |
| 0.0003 | 29.0 | 29 | 5.4983 | 0.0 |
| 0.0 | 30.0 | 30 | 5.5031 | 0.0 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
- Task
- Image Classification
- Library
- transformers
- Type
- swin
- License
- apache-2.0
- Created
- Jun 28, 2023
- Updated
- Sep 29, 2023