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simlaharma
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vit-base-cifar10

Image Classification
Transformers
PyTorch
vit
vision
Generated from Trainer
Eval Results (legacy)
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update model card README.md

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  ---
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  license: apache-2.0
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  tags:
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- - image-classification
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- - vision
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  - generated_from_trainer
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  datasets:
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  - cifar10
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9897333333333334
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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
@@ -33,8 +31,8 @@ should probably proofread and complete it, then remove this comment. -->
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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 cifar10 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0394
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- - Accuracy: 0.9897
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.3006 | 1.0 | 664 | 0.1272 | 0.9816 |
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- | 0.2816 | 2.0 | 1328 | 0.0785 | 0.9843 |
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- | 0.2165 | 3.0 | 1992 | 0.0607 | 0.9863 |
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- | 0.2401 | 4.0 | 2656 | 0.0507 | 0.9872 |
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- | 0.2458 | 5.0 | 3320 | 0.0465 | 0.9877 |
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- | 0.171 | 6.0 | 3984 | 0.0434 | 0.9889 |
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- | 0.1617 | 7.0 | 4648 | 0.0384 | 0.9903 |
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- | 0.1778 | 8.0 | 5312 | 0.0415 | 0.9893 |
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- | 0.156 | 9.0 | 5976 | 0.0403 | 0.9899 |
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- | 0.118 | 10.0 | 6640 | 0.0399 | 0.9901 |
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  ### Framework versions
 
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  ---
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  license: apache-2.0
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  tags:
 
 
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  - generated_from_trainer
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  datasets:
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  - cifar10
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9856
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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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  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 cifar10 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0496
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+ - Accuracy: 0.9856
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.4169 | 1.0 | 664 | 0.1741 | 0.9725 |
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+ | 0.3364 | 2.0 | 1328 | 0.1108 | 0.9745 |
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+ | 0.2856 | 3.0 | 1992 | 0.0864 | 0.9764 |
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+ | 0.2716 | 4.0 | 2656 | 0.0721 | 0.9811 |
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+ | 0.2738 | 5.0 | 3320 | 0.0678 | 0.9824 |
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+ | 0.2432 | 6.0 | 3984 | 0.0610 | 0.9825 |
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+ | 0.2163 | 7.0 | 4648 | 0.0603 | 0.9843 |
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+ | 0.2368 | 8.0 | 5312 | 0.0580 | 0.9835 |
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+ | 0.2236 | 9.0 | 5976 | 0.0506 | 0.9855 |
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+ | 0.1671 | 10.0 | 6640 | 0.0496 | 0.9856 |
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  ### Framework versions