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update model card README.md
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README.md
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---
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license: apache-2.0
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base_model: google/vit-base-patch16-224-in21k
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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-in21k-mobile-eye-tracking-dataset-v2
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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: 0.9885350318471338
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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-in21k-mobile-eye-tracking-dataset-v2
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0657
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- Accuracy: 0.9885
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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: 24
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- eval_batch_size: 24
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 96
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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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| 0.024 | 0.99 | 73 | 0.0769 | 0.9809 |
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| 0.0236 | 1.99 | 147 | 0.1111 | 0.9745 |
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| 0.0172 | 3.0 | 221 | 0.0542 | 0.9898 |
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| 0.0114 | 4.0 | 295 | 0.0630 | 0.9885 |
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| 0.0051 | 4.99 | 368 | 0.0674 | 0.9860 |
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| 0.0044 | 5.99 | 442 | 0.0640 | 0.9885 |
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| 0.0037 | 7.0 | 516 | 0.0646 | 0.9885 |
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| 0.0034 | 8.0 | 590 | 0.0652 | 0.9885 |
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| 0.0032 | 8.99 | 663 | 0.0656 | 0.9885 |
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| 0.0032 | 9.9 | 730 | 0.0657 | 0.9885 |
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### Framework versions
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- Transformers 4.31.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.4
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- Tokenizers 0.13.3
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