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vit-base-patch16-224-finetuned-main-gpu-20e-final-1

Image Classification
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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-finetuned-main-gpu-20e-final-1
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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: validation
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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: 0.9917517006802721
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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-finetuned-main-gpu-20e-final-1
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+
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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 the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0272
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+ - Accuracy: 0.9918
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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: 5e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 128
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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: 20
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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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+ | 0.4776 | 1.0 | 551 | 0.4399 | 0.8125 |
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+ | 0.3207 | 2.0 | 1102 | 0.2645 | 0.8978 |
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+ | 0.2292 | 3.0 | 1653 | 0.1388 | 0.9468 |
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+ | 0.1811 | 4.0 | 2204 | 0.0943 | 0.9662 |
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+ | 0.1633 | 5.0 | 2755 | 0.0740 | 0.9723 |
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+ | 0.1355 | 6.0 | 3306 | 0.0744 | 0.9727 |
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+ | 0.1413 | 7.0 | 3857 | 0.0548 | 0.9813 |
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+ | 0.1257 | 8.0 | 4408 | 0.0442 | 0.9844 |
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+ | 0.1057 | 9.0 | 4959 | 0.0517 | 0.9821 |
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+ | 0.1 | 10.0 | 5510 | 0.0376 | 0.9868 |
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+ | 0.0873 | 11.0 | 6061 | 0.0410 | 0.9866 |
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+ | 0.0974 | 12.0 | 6612 | 0.0430 | 0.9861 |
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+ | 0.0673 | 13.0 | 7163 | 0.0421 | 0.9852 |
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+ | 0.0913 | 14.0 | 7714 | 0.0339 | 0.9882 |
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+ | 0.0594 | 15.0 | 8265 | 0.0327 | 0.9896 |
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+ | 0.0608 | 16.0 | 8816 | 0.0379 | 0.9885 |
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+ | 0.0725 | 17.0 | 9367 | 0.0288 | 0.9904 |
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+ | 0.0742 | 18.0 | 9918 | 0.0284 | 0.9906 |
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+ | 0.0708 | 19.0 | 10469 | 0.0273 | 0.9916 |
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+ | 0.0648 | 20.0 | 11020 | 0.0272 | 0.9918 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.26.1
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+ - Pytorch 1.13.1+cu116
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+ - Datasets 2.10.1
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+ - Tokenizers 0.13.2