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swin-tiny-patch4-window7-224-attempt-2
cvint91/swin-tiny-patch4-window7-224-attempt-2
swin-tiny-patch4-window7-224-attempt-2 is a image classification model from cvint91. 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 6, 2023
- Updated
- Jun 7, 2023
- SHA
- ec77cc06
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-attempt-2
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0387
- F1: 0.9886
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: 4.623574902931369e-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 | F1 |
|---|---|---|---|---|
| 0.0134 | 0.99 | 33 | 0.0506 | 0.9831 |
| 0.015 | 1.98 | 66 | 0.0226 | 0.9857 |
| 0.0063 | 2.98 | 99 | 0.0563 | 0.9826 |
| 0.0009 | 4.0 | 133 | 0.0387 | 0.9886 |
| 0.0142 | 4.96 | 165 | 0.0722 | 0.9826 |
Framework versions
- Transformers 4.27.4
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
- Task
- Image Classification
- Library
- transformers
- Type
- swin
- License
- apache-2.0
- Created
- Jun 6, 2023
- Updated
- Jun 7, 2023