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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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+ - generated_from_trainer
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+ datasets:
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+ - imagefolder
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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-in21k-face-recognition
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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: Accuracy
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+ type: accuracy
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+ value: 0.999957997311828
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+ ---
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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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+
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+ # vit-base-patch16-224-in21k-face-recognition
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+
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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: 0.0015
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+ - Accuracy: 1.0000
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.00012
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 256
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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: 8
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.0368 | 1.0 | 372 | 0.0346 | 1.0000 |
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+ | 0.0094 | 2.0 | 744 | 0.0092 | 1.0000 |
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+ | 0.0046 | 3.0 | 1116 | 0.0047 | 1.0000 |
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+ | 0.0029 | 4.0 | 1488 | 0.0029 | 1.0 |
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+ | 0.0022 | 5.0 | 1860 | 0.0023 | 0.9999 |
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+ | 0.0017 | 6.0 | 2232 | 0.0017 | 1.0 |
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+ | 0.0015 | 7.0 | 2604 | 0.0015 | 1.0 |
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+ | 0.0014 | 8.0 | 2976 | 0.0015 | 1.0000 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.30.2
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+ - Pytorch 1.13.1+cu117
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+ - Datasets 2.13.2
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+ - Tokenizers 0.11.0