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End of training
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README.md
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---
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license: apache-2.0
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base_model: LaLegumbreArtificial/Fraunhofer_Classical
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tags:
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- generated_from_trainer
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datasets:
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- imagefolder
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metrics:
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- accuracy
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model-index:
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- name: Fraunhofer_Classical_multiclass_1
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results:
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- task:
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name: Image Classification
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type: image-classification
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dataset:
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name: imagefolder
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type: imagefolder
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config: default
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split: train
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.99075
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Fraunhofer_Classical_multiclass_1
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This model is a fine-tuned version of [LaLegumbreArtificial/Fraunhofer_Classical](https://huggingface.co/LaLegumbreArtificial/Fraunhofer_Classical) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0275
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- Accuracy: 0.9908
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 128
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 5
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 0.0756 | 1.0 | 146 | 0.1135 | 0.9647 |
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| 0.0435 | 2.0 | 292 | 0.0648 | 0.9785 |
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| 0.0536 | 3.0 | 438 | 0.0442 | 0.984 |
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| 0.0389 | 4.0 | 584 | 0.0285 | 0.9898 |
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| 0.0292 | 5.0 | 730 | 0.0275 | 0.9908 |
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### Framework versions
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- Transformers 4.44.0
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- Pytorch 2.4.0
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- Datasets 2.21.0
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- Tokenizers 0.19.1
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