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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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  - generated_from_trainer
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  metrics:
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  - accuracy
@@ -15,10 +14,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # vit-base-clothing-leafs-example-full-simple
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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 beans dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.6693
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- - Accuracy: 0.2481
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  ## Model description
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@@ -37,7 +36,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.002
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  - train_batch_size: 32
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  - eval_batch_size: 8
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  - seed: 42
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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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- | 2.7222 | 0.14 | 1000 | 2.6766 | 0.2528 |
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- | 2.6768 | 0.28 | 2000 | 2.6693 | 0.2481 |
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- | 2.6689 | 0.41 | 3000 | nan | 0.2230 |
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- | 0.4613 | 0.55 | 4000 | nan | 0.0512 |
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- | 0.0 | 0.69 | 5000 | nan | 0.0512 |
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- | 0.0 | 0.83 | 6000 | nan | 0.0512 |
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- | 0.0 | 0.97 | 7000 | nan | 0.0512 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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  metrics:
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  - accuracy
 
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  # vit-base-clothing-leafs-example-full-simple
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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 None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.0454
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+ - Accuracy: 0.7095
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 1e-05
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  - train_batch_size: 32
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  - eval_batch_size: 8
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  - seed: 42
 
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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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+ | 2.4732 | 0.14 | 1000 | 1.9312 | 0.5466 |
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+ | 1.7297 | 0.28 | 2000 | 1.5575 | 0.6410 |
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+ | 1.4684 | 0.41 | 3000 | 1.3885 | 0.6578 |
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+ | 1.3161 | 0.55 | 4000 | 1.2847 | 0.6701 |
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+ | 1.2652 | 0.69 | 5000 | 1.2144 | 0.6804 |
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+ | 1.1962 | 0.83 | 6000 | 1.1739 | 0.6857 |
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+ | 1.1536 | 0.97 | 7000 | 1.1354 | 0.6907 |
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+ | 1.0931 | 1.11 | 8000 | 1.1160 | 0.6935 |
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+ | 1.0486 | 1.24 | 9000 | 1.1065 | 0.6965 |
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+ | 1.0242 | 1.38 | 10000 | 1.0801 | 0.6990 |
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+ | 1.0203 | 1.52 | 11000 | 1.0678 | 0.7031 |
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+ | 1.0079 | 1.66 | 12000 | 1.0624 | 0.7038 |
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+ | 0.9962 | 1.8 | 13000 | 1.0550 | 0.7039 |
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+ | 0.9975 | 1.94 | 14000 | 1.0496 | 0.7049 |
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+ | 0.9572 | 2.07 | 15000 | 1.0451 | 0.7076 |
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+ | 0.8944 | 2.21 | 16000 | 1.0498 | 0.7076 |
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+ | 0.9027 | 2.35 | 17000 | 1.0397 | 0.7079 |
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+ | 0.8806 | 2.49 | 18000 | 1.0354 | 0.7093 |
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+ | 0.8968 | 2.63 | 19000 | 1.0362 | 0.7090 |
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+ | 0.8895 | 2.77 | 20000 | 1.0286 | 0.7106 |
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+ | 0.8764 | 2.9 | 21000 | 1.0267 | 0.7121 |
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+ | 0.8495 | 3.04 | 22000 | 1.0354 | 0.7089 |
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+ | 0.7935 | 3.18 | 23000 | 1.0451 | 0.7068 |
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+ | 0.7876 | 3.32 | 24000 | 1.0456 | 0.7097 |
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+ | 0.8093 | 3.46 | 25000 | 1.0435 | 0.7088 |
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+ | 0.7977 | 3.6 | 26000 | 1.0454 | 0.7095 |
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  ### Framework versions