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swin-base-patch4-window7-224-finetuned-lora-scenes
Liyan049/swin-base-patch4-window7-224-finetuned-lora-scenes
swin-base-patch4-window7-224-finetuned-lora-scenes is a machine learning model from Liyan049. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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Public
Updated Dec 7, 2023
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At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Dec 1, 2023
- Updated
- Dec 7, 2023
- SHA
- 89696f9e
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-base-patch4-window7-224-finetuned-lora-scenes
This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0227
- Accuracy: 0.9977
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: 0.005
- 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
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2219 | 0.99 | 31 | 0.0615 | 0.9775 |
| 0.1187 | 1.98 | 62 | 0.0448 | 0.9887 |
| 0.1298 | 2.98 | 93 | 0.0308 | 0.9910 |
| 0.0886 | 4.0 | 125 | 0.0227 | 0.9977 |
| 0.0886 | 4.96 | 155 | 0.0233 | 0.9932 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
- License
- apache-2.0
- Created
- Dec 1, 2023
- Updated
- Dec 7, 2023