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---
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license: apache-2.0
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base_model: google/vit-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: vit-base-patch16-224-U8-10
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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: validation
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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: 0.8833333333333333
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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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# vit-base-patch16-224-U8-10
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-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: 0.5606
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- Accuracy: 0.8833
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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: 5.5e-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: 0.05
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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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| 1.2473 | 1.0 | 20 | 1.1804 | 0.5833 |
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| 0.9555 | 2.0 | 40 | 0.9370 | 0.65 |
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| 0.727 | 3.0 | 60 | 0.7202 | 0.6833 |
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| 0.5288 | 4.0 | 80 | 0.5606 | 0.8833 |
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| 0.3876 | 5.0 | 100 | 0.6482 | 0.7667 |
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| 0.296 | 6.0 | 120 | 0.7458 | 0.7167 |
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| 0.236 | 7.0 | 140 | 0.4677 | 0.8833 |
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| 0.2129 | 8.0 | 160 | 0.5138 | 0.8333 |
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| 0.1781 | 9.0 | 180 | 0.4736 | 0.85 |
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| 0.1854 | 10.0 | 200 | 0.4801 | 0.8 |
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### Framework versions
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- Transformers 4.36.2
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- Pytorch 2.1.2+cu118
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- Datasets 2.16.1
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- Tokenizers 0.15.0
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