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vit-augmentation

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vit
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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
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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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+ - precision
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+ - recall
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+ - f1
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+ model-index:
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+ - name: vit-augmentation
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+ results: []
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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-augmentation
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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 an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.5668
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+ - Accuracy: 0.8804
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+ - Precision: 0.8823
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+ - Recall: 0.8804
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+ - F1: 0.8789
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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: 0.0001
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+ - train_batch_size: 16
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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: cosine
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+ - lr_scheduler_warmup_steps: 770
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+ - num_epochs: 100
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+ - mixed_precision_training: Native AMP
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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 | Precision | Recall | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 0.9124 | 1.0 | 321 | 0.6025 | 0.7805 | 0.7788 | 0.7805 | 0.7683 |
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+ | 0.5876 | 2.0 | 642 | 0.5819 | 0.7864 | 0.7990 | 0.7864 | 0.7820 |
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+ | 0.5415 | 3.0 | 963 | 0.6149 | 0.8041 | 0.7943 | 0.8041 | 0.7865 |
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+ | 0.4815 | 4.0 | 1284 | 0.4654 | 0.8294 | 0.8259 | 0.8294 | 0.8115 |
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+ | 0.4263 | 5.0 | 1605 | 0.5481 | 0.8259 | 0.8315 | 0.8259 | 0.8023 |
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+ | 0.3515 | 6.0 | 1926 | 0.4287 | 0.8592 | 0.8580 | 0.8592 | 0.8574 |
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+ | 0.3144 | 7.0 | 2247 | 0.5005 | 0.8363 | 0.8320 | 0.8363 | 0.8270 |
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+ | 0.2736 | 8.0 | 2568 | 0.5306 | 0.8294 | 0.8448 | 0.8294 | 0.8302 |
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+ | 0.2519 | 9.0 | 2889 | 0.4733 | 0.8578 | 0.8534 | 0.8578 | 0.8534 |
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+ | 0.2227 | 10.0 | 3210 | 0.4905 | 0.8585 | 0.8520 | 0.8585 | 0.8512 |
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+ | 0.1724 | 11.0 | 3531 | 0.5050 | 0.8655 | 0.8671 | 0.8655 | 0.8628 |
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+ | 0.1596 | 12.0 | 3852 | 0.5263 | 0.8686 | 0.8657 | 0.8686 | 0.8631 |
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+ | 0.1397 | 13.0 | 4173 | 0.7043 | 0.8533 | 0.8703 | 0.8533 | 0.8488 |
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+ | 0.1298 | 14.0 | 4494 | 0.6275 | 0.8679 | 0.8734 | 0.8679 | 0.8632 |
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+ | 0.1029 | 15.0 | 4815 | 0.5564 | 0.8807 | 0.8776 | 0.8807 | 0.8772 |
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+ | 0.0893 | 16.0 | 5136 | 0.5668 | 0.8804 | 0.8823 | 0.8804 | 0.8789 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.40.0.dev0
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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