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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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metrics:
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- accuracy
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model-index:
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- name: vit-base-patch16-224-mascotas
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results: []
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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-mascotas
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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 an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5660
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- Accuracy: 1.0
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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: 8
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 16
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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: 40
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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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| 0.6192 | 0.99 | 52 | 0.5660 | 1.0 |
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| 0.2794 | 2.0 | 105 | 0.1524 | 1.0 |
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| 0.2006 | 2.99 | 157 | 0.1143 | 0.9474 |
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| 0.2106 | 4.0 | 210 | 0.0506 | 1.0 |
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| 0.1077 | 4.99 | 262 | 0.0339 | 1.0 |
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| 0.1379 | 6.0 | 315 | 0.1391 | 0.9474 |
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| 0.1387 | 6.99 | 367 | 0.0711 | 0.9474 |
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| 0.1342 | 8.0 | 420 | 0.0231 | 1.0 |
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| 0.0803 | 8.99 | 472 | 0.0475 | 1.0 |
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| 0.097 | 10.0 | 525 | 0.0190 | 1.0 |
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| 0.0888 | 10.99 | 577 | 0.0220 | 1.0 |
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| 0.0668 | 12.0 | 630 | 0.0078 | 1.0 |
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| 0.0559 | 12.99 | 682 | 0.0073 | 1.0 |
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| 0.0759 | 14.0 | 735 | 0.0055 | 1.0 |
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| 0.081 | 14.99 | 787 | 0.0058 | 1.0 |
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| 0.0806 | 16.0 | 840 | 0.0102 | 1.0 |
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| 0.0568 | 16.99 | 892 | 0.0164 | 1.0 |
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| 0.0696 | 18.0 | 945 | 0.0049 | 1.0 |
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| 0.0692 | 18.99 | 997 | 0.0040 | 1.0 |
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| 0.0929 | 20.0 | 1050 | 0.0030 | 1.0 |
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| 0.1169 | 20.99 | 1102 | 0.0183 | 1.0 |
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| 0.0385 | 22.0 | 1155 | 0.0576 | 0.9474 |
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| 0.0564 | 22.99 | 1207 | 0.0512 | 0.9474 |
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| 0.0206 | 24.0 | 1260 | 0.0025 | 1.0 |
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| 0.0984 | 24.99 | 1312 | 0.0028 | 1.0 |
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| 0.0368 | 26.0 | 1365 | 0.0037 | 1.0 |
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| 0.0436 | 26.99 | 1417 | 0.0609 | 0.9474 |
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| 0.0896 | 28.0 | 1470 | 0.0154 | 1.0 |
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| 0.079 | 28.99 | 1522 | 0.0026 | 1.0 |
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| 0.0211 | 30.0 | 1575 | 0.0045 | 1.0 |
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| 0.0499 | 30.99 | 1627 | 0.0042 | 1.0 |
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| 0.0137 | 32.0 | 1680 | 0.0043 | 1.0 |
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| 0.0711 | 32.99 | 1732 | 0.0019 | 1.0 |
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| 0.0369 | 34.0 | 1785 | 0.0021 | 1.0 |
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| 0.0382 | 34.99 | 1837 | 0.0031 | 1.0 |
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| 0.0785 | 36.0 | 1890 | 0.0029 | 1.0 |
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| 0.0575 | 36.99 | 1942 | 0.0020 | 1.0 |
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| 0.0158 | 38.0 | 1995 | 0.0019 | 1.0 |
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| 0.0489 | 38.99 | 2047 | 0.0022 | 1.0 |
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| 0.0511 | 39.62 | 2080 | 0.0023 | 1.0 |
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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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