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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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metrics: |
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- accuracy |
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model-index: |
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- name: bachh |
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results: [] |
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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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# bachh |
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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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5198 |
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- Accuracy: 0.8654 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 32 |
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- eval_batch_size: 16 |
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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: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.8279 | 0.11 | 100 | 0.9659 | 0.6697 | |
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| 0.7801 | 0.22 | 200 | 0.8758 | 0.6725 | |
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| 0.7136 | 0.34 | 300 | 0.7788 | 0.7178 | |
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| 0.7302 | 0.45 | 400 | 0.7041 | 0.7438 | |
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| 0.6793 | 0.56 | 500 | 0.6882 | 0.7431 | |
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| 0.5872 | 0.67 | 600 | 0.6480 | 0.7683 | |
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| 0.5711 | 0.78 | 700 | 0.6490 | 0.7588 | |
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| 0.6003 | 0.9 | 800 | 0.6116 | 0.7727 | |
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| 0.4382 | 1.01 | 900 | 0.6016 | 0.7883 | |
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| 0.4666 | 1.12 | 1000 | 0.5848 | 0.7803 | |
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| 0.3944 | 1.23 | 1100 | 0.6039 | 0.7745 | |
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| 0.3805 | 1.35 | 1200 | 0.4929 | 0.8221 | |
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| 0.3795 | 1.46 | 1300 | 0.5391 | 0.8048 | |
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| 0.3727 | 1.57 | 1400 | 0.5303 | 0.8149 | |
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| 0.3658 | 1.68 | 1500 | 0.5471 | 0.8160 | |
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| 0.3289 | 1.79 | 1600 | 0.5170 | 0.8184 | |
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| 0.2832 | 1.91 | 1700 | 0.4795 | 0.8334 | |
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| 0.2048 | 2.02 | 1800 | 0.4942 | 0.8300 | |
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| 0.2085 | 2.13 | 1900 | 0.4743 | 0.8394 | |
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| 0.1449 | 2.24 | 2000 | 0.4642 | 0.8469 | |
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| 0.1662 | 2.35 | 2100 | 0.4669 | 0.8426 | |
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| 0.1603 | 2.47 | 2200 | 0.4721 | 0.8452 | |
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| 0.1079 | 2.58 | 2300 | 0.5236 | 0.8342 | |
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| 0.1952 | 2.69 | 2400 | 0.4448 | 0.8493 | |
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| 0.2091 | 2.8 | 2500 | 0.4959 | 0.8518 | |
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| 0.166 | 2.91 | 2600 | 0.5036 | 0.8356 | |
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| 0.055 | 3.03 | 2700 | 0.4854 | 0.8497 | |
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| 0.0639 | 3.14 | 2800 | 0.4987 | 0.8605 | |
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| 0.0421 | 3.25 | 2900 | 0.5258 | 0.8543 | |
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| 0.0292 | 3.36 | 3000 | 0.5287 | 0.8601 | |
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| 0.0136 | 3.48 | 3100 | 0.5250 | 0.8593 | |
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| 0.0421 | 3.59 | 3200 | 0.5192 | 0.8647 | |
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| 0.0307 | 3.7 | 3300 | 0.5251 | 0.8569 | |
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| 0.0536 | 3.81 | 3400 | 0.5300 | 0.8611 | |
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| 0.0269 | 3.92 | 3500 | 0.5198 | 0.8654 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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