File size: 4,509 Bytes
e066afe |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-brain-tumor-detection
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-brain-tumor-detection
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.
It achieves the following results on the evaluation set:
- Loss: 0.5832
- Accuracy: 0.785
## 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9535 | 0.4 | 100 | 0.8966 | 0.618 |
| 0.862 | 0.8 | 200 | 1.1149 | 0.561 |
| 0.7373 | 1.2 | 300 | 0.8543 | 0.605 |
| 0.6476 | 1.6 | 400 | 0.7307 | 0.666 |
| 0.6712 | 2.0 | 500 | 0.6954 | 0.694 |
| 0.4892 | 2.4 | 600 | 0.6391 | 0.707 |
| 0.5801 | 2.8 | 700 | 0.6247 | 0.708 |
| 0.3505 | 3.2 | 800 | 0.6056 | 0.778 |
| 0.3503 | 3.6 | 900 | 0.6264 | 0.743 |
| 0.3416 | 4.0 | 1000 | 0.5832 | 0.785 |
| 0.1427 | 4.4 | 1100 | 0.7297 | 0.769 |
| 0.1982 | 4.8 | 1200 | 0.7761 | 0.73 |
| 0.193 | 5.2 | 1300 | 0.8467 | 0.741 |
| 0.1831 | 5.6 | 1400 | 0.6975 | 0.774 |
| 0.2612 | 6.0 | 1500 | 0.8719 | 0.775 |
| 0.102 | 6.4 | 1600 | 0.9045 | 0.788 |
| 0.1029 | 6.8 | 1700 | 0.9655 | 0.783 |
| 0.0735 | 7.2 | 1800 | 0.9906 | 0.78 |
| 0.0715 | 7.6 | 1900 | 0.8893 | 0.787 |
| 0.1254 | 8.0 | 2000 | 1.1221 | 0.761 |
| 0.021 | 8.4 | 2100 | 1.1648 | 0.779 |
| 0.0133 | 8.8 | 2200 | 0.9857 | 0.806 |
| 0.0086 | 9.2 | 2300 | 1.0365 | 0.799 |
| 0.0223 | 9.6 | 2400 | 0.9826 | 0.812 |
| 0.0023 | 10.0 | 2500 | 1.0697 | 0.795 |
| 0.0021 | 10.4 | 2600 | 1.0490 | 0.815 |
| 0.0401 | 10.8 | 2700 | 1.1594 | 0.8 |
| 0.0012 | 11.2 | 2800 | 1.0811 | 0.817 |
| 0.0034 | 11.6 | 2900 | 1.0956 | 0.825 |
| 0.0012 | 12.0 | 3000 | 1.2010 | 0.808 |
| 0.0011 | 12.4 | 3100 | 1.1712 | 0.81 |
| 0.0092 | 12.8 | 3200 | 1.1814 | 0.813 |
| 0.0007 | 13.2 | 3300 | 1.1677 | 0.818 |
| 0.0007 | 13.6 | 3400 | 1.1723 | 0.818 |
| 0.0006 | 14.0 | 3500 | 1.1852 | 0.821 |
| 0.0005 | 14.4 | 3600 | 1.1928 | 0.82 |
| 0.0005 | 14.8 | 3700 | 1.2030 | 0.819 |
| 0.0005 | 15.2 | 3800 | 1.2093 | 0.818 |
| 0.0005 | 15.6 | 3900 | 1.2160 | 0.818 |
| 0.0004 | 16.0 | 4000 | 1.2232 | 0.819 |
| 0.0004 | 16.4 | 4100 | 1.2302 | 0.819 |
| 0.0004 | 16.8 | 4200 | 1.2350 | 0.819 |
| 0.0004 | 17.2 | 4300 | 1.2400 | 0.82 |
| 0.0004 | 17.6 | 4400 | 1.2442 | 0.821 |
| 0.0004 | 18.0 | 4500 | 1.2483 | 0.821 |
| 0.0004 | 18.4 | 4600 | 1.2518 | 0.821 |
| 0.0004 | 18.8 | 4700 | 1.2546 | 0.821 |
| 0.0004 | 19.2 | 4800 | 1.2561 | 0.821 |
| 0.0004 | 19.6 | 4900 | 1.2574 | 0.82 |
| 0.0004 | 20.0 | 5000 | 1.2577 | 0.82 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|