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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-ve-U12-b-24
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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: 0.8478260869565217
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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-ve-U12-b-24
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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: 0.6456
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- Accuracy: 0.8478
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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: 24
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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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| No log | 0.92 | 6 | 1.3806 | 0.4130 |
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| 1.379 | 2.0 | 13 | 1.3103 | 0.5435 |
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| 1.379 | 2.92 | 19 | 1.2269 | 0.4130 |
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| 1.2758 | 4.0 | 26 | 1.1412 | 0.4565 |
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| 1.121 | 4.92 | 32 | 1.0650 | 0.4783 |
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| 1.121 | 6.0 | 39 | 1.0084 | 0.5217 |
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| 0.9871 | 6.92 | 45 | 0.9395 | 0.6522 |
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| 0.8612 | 8.0 | 52 | 0.8798 | 0.7174 |
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| 0.8612 | 8.92 | 58 | 0.8219 | 0.7391 |
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| 0.7653 | 10.0 | 65 | 0.7712 | 0.7826 |
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| 0.6674 | 10.92 | 71 | 0.7328 | 0.7609 |
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| 0.6674 | 12.0 | 78 | 0.6968 | 0.7391 |
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| 0.568 | 12.92 | 84 | 0.6456 | 0.8478 |
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| 0.4723 | 14.0 | 91 | 0.6528 | 0.8043 |
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| 0.4723 | 14.92 | 97 | 0.7107 | 0.6739 |
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| 0.4256 | 16.0 | 104 | 0.6335 | 0.7609 |
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| 0.3524 | 16.92 | 110 | 0.5953 | 0.8261 |
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| 0.3524 | 18.0 | 117 | 0.5824 | 0.8261 |
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| 0.3282 | 18.92 | 123 | 0.6329 | 0.7174 |
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| 0.3074 | 20.0 | 130 | 0.5775 | 0.8043 |
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| 0.3074 | 20.92 | 136 | 0.5770 | 0.8043 |
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| 0.3076 | 22.0 | 143 | 0.5749 | 0.8261 |
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| 0.3076 | 22.15 | 144 | 0.5747 | 0.8261 |
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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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