Model save

README.md

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 ---
-library_name: transformers
-tags: []
+license: apache-2.0
+base_model: google/vit-base-patch16-224
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: vit-base-PICAI
+  results: []
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
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-## Uses
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-### Direct Use
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-### Recommendations
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-### Training Procedure 
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-## Evaluation
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-### Results
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-#### Summary
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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-## Glossary [optional]
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+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# vit-base-PICAI
+
+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.
+It achieves the following results on the evaluation set:
+- Loss: 0.6043
+- Accuracy: 0.7371
+- Roc Auc: 0.7059
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 4
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Roc Auc |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:|
+| 0.4995        | 0.14  | 50   | 0.5423          | 0.7371   | 0.7072  |
+| 0.4729        | 0.29  | 100  | 0.6259          | 0.7314   | 0.7183  |
+| 0.5558        | 0.43  | 150  | 0.5564          | 0.7243   | 0.7189  |
+| 0.5825        | 0.57  | 200  | 0.5912          | 0.6943   | 0.7177  |
+| 0.5091        | 0.71  | 250  | 0.5656          | 0.73     | 0.7140  |
+| 0.4575        | 0.86  | 300  | 0.5846          | 0.7386   | 0.6858  |
+| 0.5168        | 1.0   | 350  | 0.5363          | 0.7471   | 0.7076  |
+| 0.5305        | 1.14  | 400  | 0.5600          | 0.7357   | 0.7042  |
+| 0.4275        | 1.29  | 450  | 0.5864          | 0.7357   | 0.6988  |
+| 0.5588        | 1.43  | 500  | 0.5477          | 0.75     | 0.7078  |
+| 0.4573        | 1.57  | 550  | 0.5321          | 0.7571   | 0.7253  |
+| 0.5094        | 1.71  | 600  | 0.5840          | 0.7457   | 0.7054  |
+| 0.5311        | 1.86  | 650  | 0.5719          | 0.7229   | 0.7098  |
+| 0.4582        | 2.0   | 700  | 0.5439          | 0.7357   | 0.7062  |
+| 0.5142        | 2.14  | 750  | 0.6668          | 0.6629   | 0.6899  |
+| 0.3833        | 2.29  | 800  | 0.5705          | 0.7286   | 0.6954  |
+| 0.4676        | 2.43  | 850  | 0.6152          | 0.6943   | 0.6795  |
+| 0.4682        | 2.57  | 900  | 0.5679          | 0.7443   | 0.7077  |
+| 0.4112        | 2.71  | 950  | 0.5600          | 0.7329   | 0.7073  |
+| 0.5107        | 2.86  | 1000 | 0.5686          | 0.7343   | 0.7017  |
+| 0.4078        | 3.0   | 1050 | 0.6165          | 0.7429   | 0.7168  |
+| 0.479         | 3.14  | 1100 | 0.5952          | 0.7257   | 0.7004  |
+| 0.3704        | 3.29  | 1150 | 0.5937          | 0.7314   | 0.6980  |
+| 0.3733        | 3.43  | 1200 | 0.5923          | 0.7214   | 0.7001  |
+| 0.3682        | 3.57  | 1250 | 0.6183          | 0.7429   | 0.6963  |
+| 0.3283        | 3.71  | 1300 | 0.6130          | 0.73     | 0.7012  |
+| 0.3709        | 3.86  | 1350 | 0.6123          | 0.74     | 0.7045  |
+| 0.3859        | 4.0   | 1400 | 0.6043          | 0.7371   | 0.7059  |
+
+
+### Framework versions
+
+- Transformers 4.39.1
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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