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Fraunhofer_Classical_multiclass_unbalaced
ricardoSLabs/Fraunhofer_Classical_multiclass_unbalaced
Fraunhofer_Classical_multiclass_unbalaced is a machine learning model from ricardoSLabs. 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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At a glance
- License
- apache-2.0
- Model type
- beit
- Access
- Public
- Created
- Aug 29, 2024
- Updated
- Aug 29, 2024
- SHA
- 28065bbb
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. -->
Fraunhofer_Classical_multiclass_unbalaced
This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0338
- Accuracy: 0.9884
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: 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 | Accuracy |
|---|---|---|---|---|
| 0.1211 | 1.0 | 146 | 0.1030 | 0.9641 |
| 0.079 | 2.0 | 292 | 0.0697 | 0.975 |
| 0.0581 | 3.0 | 438 | 0.0496 | 0.981 |
| 0.0334 | 4.0 | 584 | 0.0373 | 0.9869 |
| 0.0362 | 5.0 | 730 | 0.0338 | 0.9884 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
- Type
- beit
- License
- apache-2.0
- Created
- Aug 29, 2024
- Updated
- Aug 29, 2024