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swin-tiny-bmr-tuned
akuazzam/swin-tiny-bmr-tuned
swin-tiny-bmr-tuned is a image classification model from akuazzam. 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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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-bmr-tuned
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: 0.9305
- Accuracy: 0.8765
- Precision: 0.8810
- Recall: 0.8765
- F1: 0.8766
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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 160 | 0.3941 | 0.8519 | 0.8562 | 0.8519 | 0.8519 |
| No log | 2.0 | 320 | 0.9611 | 0.7901 | 0.8162 | 0.7901 | 0.7880 |
| No log | 3.0 | 480 | 0.8992 | 0.8272 | 0.8505 | 0.8272 | 0.8258 |
| 0.3748 | 4.0 | 640 | 0.9305 | 0.8765 | 0.8810 | 0.8765 | 0.8766 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Task
- Image Classification
- Library
- transformers
- Type
- swin
- License
- apache-2.0
- Created
- Nov 17, 2025
- Updated
- Nov 17, 2025