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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: google/vit-base-patch16-224-in21k
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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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+ - f1
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+ - precision
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+ - recall
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+ model-index:
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+ - name: vit-base-patch16-224-in21k
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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: 0.9996377394944453
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+ - name: F1
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+ type: f1
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+ value: 0.9997981836528759
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+ - name: Precision
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+ type: precision
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+ value: 0.9997309296381004
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+ - name: Recall
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+ type: recall
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+ value: 0.9998654467168999
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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
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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: 0.0025
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+ - Accuracy: 0.9996
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+ - F1: 0.9998
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+ - Precision: 0.9997
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+ - Recall: 0.9999
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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: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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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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+ - num_epochs: 3.0
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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 | F1 | Precision | Recall |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | 0.007 | 1.0 | 3173 | 0.0108 | 0.9972 | 0.9984 | 0.9969 | 1.0 |
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+ | 0.0015 | 2.0 | 6346 | 0.0022 | 0.9997 | 0.9998 | 0.9999 | 0.9998 |
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+ | 0.0 | 3.0 | 9519 | 0.0025 | 0.9996 | 0.9998 | 0.9997 | 0.9999 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.36.2
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+ - Pytorch 2.1.2+cu121
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+ - Datasets 2.15.0
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+ - Tokenizers 0.15.0
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