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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_fold2
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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: 0.843291881508442
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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_fold2
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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: 1.4322
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- Accuracy: 0.8433
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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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| 0.3441 | 1.0 | 2466 | 0.3930 | 0.8392 |
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| 0.3299 | 2.0 | 4932 | 0.3836 | 0.8474 |
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| 0.2436 | 3.0 | 7398 | 0.4043 | 0.8499 |
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| 0.2235 | 4.0 | 9864 | 0.5186 | 0.8408 |
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| 0.178 | 5.0 | 12330 | 0.6810 | 0.8485 |
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| 0.1455 | 6.0 | 14796 | 0.8839 | 0.8442 |
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| 0.1409 | 7.0 | 17262 | 1.1134 | 0.8424 |
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| 0.181 | 8.0 | 19728 | 1.3633 | 0.8364 |
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| 0.0965 | 9.0 | 22194 | 1.4369 | 0.8388 |
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| 0.0314 | 10.0 | 24660 | 1.4322 | 0.8433 |
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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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