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---
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license: apache-2.0
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base_model: microsoft/beit-base-patch16-224
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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: Karma_3Class_RMSprop_1-e5_10Epoch_Beit-base-patch16_fold3
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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: test
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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: 0.8413276664642785
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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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# Karma_3Class_RMSprop_1-e5_10Epoch_Beit-base-patch16_fold3
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This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.4124
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- Accuracy: 0.8413
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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: 1e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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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: 10
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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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| 0.4 | 1.0 | 2467 | 0.4178 | 0.8301 |
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| 0.3541 | 2.0 | 4934 | 0.3989 | 0.8425 |
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| 0.2286 | 3.0 | 7401 | 0.4379 | 0.8463 |
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| 0.2173 | 4.0 | 9868 | 0.4932 | 0.8420 |
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| 0.0599 | 5.0 | 12335 | 0.7103 | 0.8417 |
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| 0.0547 | 6.0 | 14802 | 0.9909 | 0.8426 |
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| 0.0268 | 7.0 | 17269 | 1.2232 | 0.8431 |
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| 0.0075 | 8.0 | 19736 | 1.2967 | 0.8438 |
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| 0.02 | 9.0 | 22203 | 1.3707 | 0.8407 |
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| 0.0237 | 10.0 | 24670 | 1.4124 | 0.8413 |
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### Framework versions
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- Transformers 4.35.0
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- Pytorch 2.1.0
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- Datasets 2.14.6
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- Tokenizers 0.14.1
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