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vit-base-mri
raedinkhaled/vit-base-mri
vit-base-mri is a image classification model from raedinkhaled. 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
- vit
- Access
- Public
- Created
- Jun 18, 2022
- Updated
- Jun 18, 2022
- SHA
- e2a64e0c
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. -->
vit-base-mri
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the mriDataSet dataset. It achieves the following results on the evaluation set:
- Loss: 0.0453
- Accuracy: 0.9827
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.04 | 0.3 | 500 | 0.0828 | 0.9690 |
| 0.0765 | 0.59 | 1000 | 0.0623 | 0.9750 |
| 0.0479 | 0.89 | 1500 | 0.0453 | 0.9827 |
| 0.0199 | 1.18 | 2000 | 0.0524 | 0.9857 |
| 0.0114 | 1.48 | 2500 | 0.0484 | 0.9861 |
| 0.008 | 1.78 | 3000 | 0.0566 | 0.9852 |
| 0.0051 | 2.07 | 3500 | 0.0513 | 0.9874 |
| 0.0008 | 2.37 | 4000 | 0.0617 | 0.9874 |
| 0.0021 | 2.66 | 4500 | 0.0664 | 0.9870 |
| 0.0005 | 2.96 | 5000 | 0.0639 | 0.9872 |
| 0.001 | 3.25 | 5500 | 0.0644 | 0.9879 |
| 0.0004 | 3.55 | 6000 | 0.0672 | 0.9875 |
| 0.0003 | 3.85 | 6500 | 0.0690 | 0.9879 |
Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- Jun 18, 2022
- Updated
- Jun 18, 2022