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vit-weight-decay-1e-2

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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-weight-decay-1e-2
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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-weight-decay-1e-2
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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: 0.5839
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+ - Accuracy: 0.8786
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+ - Precision: 0.8753
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+ - Recall: 0.8786
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+ - F1: 0.8761
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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: 1219
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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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+ | 1.7124 | 1.0 | 321 | 0.8697 | 0.6924 | 0.6656 | 0.6924 | 0.6030 |
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+ | 1.1476 | 2.0 | 642 | 0.7271 | 0.6990 | 0.7684 | 0.6990 | 0.7149 |
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+ | 1.0734 | 3.0 | 963 | 0.6441 | 0.7687 | 0.7568 | 0.7687 | 0.7417 |
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+ | 1.0271 | 4.0 | 1284 | 0.5855 | 0.7774 | 0.7883 | 0.7774 | 0.7814 |
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+ | 0.9158 | 5.0 | 1605 | 0.7002 | 0.7635 | 0.7930 | 0.7635 | 0.7662 |
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+ | 0.9167 | 6.0 | 1926 | 0.5867 | 0.7812 | 0.8065 | 0.7812 | 0.7900 |
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+ | 0.786 | 7.0 | 2247 | 0.6517 | 0.7340 | 0.8047 | 0.7340 | 0.7515 |
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+ | 0.7406 | 8.0 | 2568 | 0.6647 | 0.7067 | 0.8134 | 0.7067 | 0.7330 |
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+ | 0.682 | 9.0 | 2889 | 0.5106 | 0.8228 | 0.8231 | 0.8228 | 0.8207 |
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+ | 0.6427 | 10.0 | 3210 | 0.5032 | 0.8166 | 0.8354 | 0.8166 | 0.8222 |
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+ | 0.5663 | 11.0 | 3531 | 0.5358 | 0.8152 | 0.8326 | 0.8152 | 0.8216 |
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+ | 0.5395 | 12.0 | 3852 | 0.5488 | 0.8249 | 0.8392 | 0.8249 | 0.8299 |
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+ | 0.4468 | 13.0 | 4173 | 0.5790 | 0.8232 | 0.8397 | 0.8232 | 0.8260 |
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+ | 0.4247 | 14.0 | 4494 | 0.5438 | 0.8415 | 0.8570 | 0.8415 | 0.8449 |
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+ | 0.3495 | 15.0 | 4815 | 0.5135 | 0.8454 | 0.8519 | 0.8454 | 0.8467 |
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+ | 0.3039 | 16.0 | 5136 | 0.5631 | 0.8408 | 0.8520 | 0.8408 | 0.8448 |
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+ | 0.2602 | 17.0 | 5457 | 0.4994 | 0.8603 | 0.8618 | 0.8603 | 0.8600 |
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+ | 0.2616 | 18.0 | 5778 | 0.5406 | 0.8564 | 0.8622 | 0.8564 | 0.8585 |
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+ | 0.1876 | 19.0 | 6099 | 0.5612 | 0.8481 | 0.8629 | 0.8481 | 0.8525 |
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+ | 0.2052 | 20.0 | 6420 | 0.6803 | 0.8429 | 0.8502 | 0.8429 | 0.8428 |
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+ | 0.1533 | 21.0 | 6741 | 0.5464 | 0.8734 | 0.8698 | 0.8734 | 0.8709 |
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+ | 0.1175 | 22.0 | 7062 | 0.5573 | 0.8686 | 0.8667 | 0.8686 | 0.8673 |
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+ | 0.1218 | 23.0 | 7383 | 0.6043 | 0.8703 | 0.8681 | 0.8703 | 0.8669 |
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+ | 0.114 | 24.0 | 7704 | 0.5945 | 0.8710 | 0.8706 | 0.8710 | 0.8693 |
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+ | 0.104 | 25.0 | 8025 | 0.5850 | 0.8766 | 0.8753 | 0.8766 | 0.8752 |
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+ | 0.0752 | 26.0 | 8346 | 0.5868 | 0.8783 | 0.8747 | 0.8783 | 0.8757 |
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+ | 0.1309 | 27.0 | 8667 | 0.5839 | 0.8786 | 0.8753 | 0.8786 | 0.8761 |
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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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