finetuned-bin
This model is a fine-tuned version of google/vit-base-patch16-224 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.7526
- Accuracy: 0.0582
- F1: 0.0356
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
---|---|---|---|---|---|
2.7759 | 0.13 | 100 | 2.7526 | 0.0582 | 0.0356 |
2.7726 | 0.25 | 200 | 2.7794 | 0.0627 | 0.0208 |
2.7741 | 0.38 | 300 | 2.7754 | 0.0853 | 0.0450 |
2.775 | 0.5 | 400 | 2.7739 | 0.0818 | 0.0357 |
2.7738 | 0.63 | 500 | 2.7691 | 0.1204 | 0.0434 |
2.7737 | 0.75 | 600 | 2.7744 | 0.0573 | 0.0349 |
2.7732 | 0.88 | 700 | 2.7759 | 0.0484 | 0.0244 |
2.7746 | 1.0 | 800 | 2.7592 | 0.0942 | 0.0434 |
2.7737 | 1.13 | 900 | 2.7727 | 0.1098 | 0.0541 |
2.7715 | 1.25 | 1000 | 2.7719 | 0.0893 | 0.0414 |
2.7742 | 1.38 | 1100 | 2.7985 | 0.0 | 0.0 |
2.7715 | 1.51 | 1200 | 2.7729 | 0.024 | 0.0158 |
2.7698 | 1.63 | 1300 | 2.7711 | 0.0649 | 0.0344 |
2.7717 | 1.76 | 1400 | 2.7709 | 0.0858 | 0.0387 |
2.7708 | 1.88 | 1500 | 2.7726 | 0.0587 | 0.0368 |
2.7736 | 2.01 | 1600 | 2.8029 | 0.0 | 0.0 |
2.7726 | 2.13 | 1700 | 2.7743 | 0.088 | 0.0327 |
2.7734 | 2.26 | 1800 | 2.7734 | 0.0284 | 0.0163 |
2.7726 | 2.38 | 1900 | 2.7731 | 0.0578 | 0.0330 |
2.7733 | 2.51 | 2000 | 2.7711 | 0.1098 | 0.0498 |
2.7729 | 2.63 | 2100 | 2.7769 | 0.0018 | 0.0019 |
2.773 | 2.76 | 2200 | 2.7631 | 0.1076 | 0.0319 |
2.7743 | 2.89 | 2300 | 2.7768 | 0.0 | 0.0 |
2.7736 | 3.01 | 2400 | 2.7883 | 0.0 | 0.0 |
2.7726 | 3.14 | 2500 | 2.7727 | 0.104 | 0.0495 |
2.7727 | 3.26 | 2600 | 2.7694 | 0.0969 | 0.0569 |
2.7733 | 3.39 | 2700 | 2.7861 | 0.0004 | 0.0008 |
2.7739 | 3.51 | 2800 | 2.7727 | 0.1022 | 0.0575 |
2.7734 | 3.64 | 2900 | 2.7728 | 0.112 | 0.0478 |
2.7732 | 3.76 | 3000 | 2.7729 | 0.1236 | 0.0567 |
2.7736 | 3.89 | 3100 | 2.7727 | 0.1218 | 0.0587 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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