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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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- f1
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model-index:
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- name: vit-base-patch16-224-in21k
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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: F1
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type: f1
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value: 0.960503161050642
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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
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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: 0.0377
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- F1: 0.9605
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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: 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: 0.1
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- num_epochs: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:------:|
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| 0.1855 | 0.99 | 53 | 0.1819 | 0.4851 |
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| 0.1147 | 1.99 | 107 | 0.1140 | 0.7505 |
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| 0.1075 | 3.0 | 161 | 0.0932 | 0.8654 |
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| 0.0755 | 4.0 | 215 | 0.0684 | 0.9268 |
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| 0.0605 | 4.99 | 268 | 0.0584 | 0.9294 |
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| 0.0475 | 5.99 | 322 | 0.0436 | 0.9550 |
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| 0.0442 | 7.0 | 376 | 0.0503 | 0.9367 |
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| 0.0464 | 8.0 | 430 | 0.0398 | 0.9599 |
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| 0.0267 | 8.99 | 483 | 0.0445 | 0.9423 |
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| 0.0374 | 9.86 | 530 | 0.0377 | 0.9605 |
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### Framework versions
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- Transformers 4.37.2
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- Pytorch 1.12.1+cu102
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- Datasets 2.16.1
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- Tokenizers 0.15.1
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