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vit-beta1-0.85

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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-beta1-0.85
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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-beta1-0.85
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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: 0.6038
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+ - Accuracy: 0.8766
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+ - Precision: 0.8722
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+ - Recall: 0.8766
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+ - F1: 0.8729
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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=(0.85,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_steps: 1733
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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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+ | 1.763 | 1.0 | 321 | 0.9505 | 0.6952 | 0.6346 | 0.6952 | 0.6202 |
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+ | 1.149 | 2.0 | 642 | 0.7147 | 0.7445 | 0.7457 | 0.7445 | 0.7230 |
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+ | 1.0452 | 3.0 | 963 | 0.6250 | 0.7573 | 0.7591 | 0.7573 | 0.7321 |
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+ | 1.0048 | 4.0 | 1284 | 0.5614 | 0.7784 | 0.7792 | 0.7784 | 0.7737 |
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+ | 0.931 | 5.0 | 1605 | 0.6082 | 0.7739 | 0.8020 | 0.7739 | 0.7823 |
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+ | 0.9808 | 6.0 | 1926 | 0.5542 | 0.7982 | 0.7984 | 0.7982 | 0.7951 |
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+ | 0.8908 | 7.0 | 2247 | 0.5957 | 0.7545 | 0.8202 | 0.7545 | 0.7709 |
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+ | 0.7747 | 8.0 | 2568 | 0.5766 | 0.7694 | 0.8155 | 0.7694 | 0.7836 |
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+ | 0.741 | 9.0 | 2889 | 0.5431 | 0.7996 | 0.8190 | 0.7996 | 0.8047 |
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+ | 0.7179 | 10.0 | 3210 | 0.5865 | 0.7774 | 0.8313 | 0.7774 | 0.7904 |
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+ | 0.6102 | 11.0 | 3531 | 0.5288 | 0.8096 | 0.8361 | 0.8096 | 0.8180 |
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+ | 0.574 | 12.0 | 3852 | 0.5991 | 0.7996 | 0.8332 | 0.7996 | 0.8096 |
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+ | 0.4515 | 13.0 | 4173 | 0.5890 | 0.8370 | 0.8334 | 0.8370 | 0.8293 |
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+ | 0.4629 | 14.0 | 4494 | 0.5573 | 0.8121 | 0.8463 | 0.8121 | 0.8205 |
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+ | 0.3927 | 15.0 | 4815 | 0.5279 | 0.8332 | 0.8506 | 0.8332 | 0.8357 |
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+ | 0.3535 | 16.0 | 5136 | 0.5364 | 0.8356 | 0.8494 | 0.8356 | 0.8405 |
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+ | 0.2635 | 17.0 | 5457 | 0.5475 | 0.8547 | 0.8626 | 0.8547 | 0.8532 |
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+ | 0.2493 | 18.0 | 5778 | 0.5102 | 0.8558 | 0.8568 | 0.8558 | 0.8553 |
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+ | 0.2125 | 19.0 | 6099 | 0.6120 | 0.8329 | 0.8623 | 0.8329 | 0.8418 |
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+ | 0.2179 | 20.0 | 6420 | 0.5721 | 0.8568 | 0.8563 | 0.8568 | 0.8563 |
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+ | 0.1598 | 21.0 | 6741 | 0.5503 | 0.8651 | 0.8623 | 0.8651 | 0.8633 |
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+ | 0.1194 | 22.0 | 7062 | 0.5829 | 0.8679 | 0.8672 | 0.8679 | 0.8669 |
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+ | 0.1245 | 23.0 | 7383 | 0.6138 | 0.8682 | 0.8632 | 0.8682 | 0.8629 |
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+ | 0.1239 | 24.0 | 7704 | 0.6136 | 0.8731 | 0.8703 | 0.8731 | 0.8695 |
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+ | 0.1159 | 25.0 | 8025 | 0.5931 | 0.8752 | 0.8724 | 0.8752 | 0.8726 |
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+ | 0.089 | 26.0 | 8346 | 0.5847 | 0.8776 | 0.8743 | 0.8776 | 0.8750 |
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+ | 0.1123 | 27.0 | 8667 | 0.5941 | 0.8752 | 0.8710 | 0.8752 | 0.8719 |
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+ | 0.0779 | 28.0 | 8988 | 0.6038 | 0.8766 | 0.8722 | 0.8766 | 0.8729 |
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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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