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seizure_vit_jlb_231027
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the JLB-JLB/seizure_eeg_greyscale_224x224_6secWindow_adjusted dataset. It achieves the following results on the evaluation set:
- Loss: 0.4759
- Roc Auc: 0.7822
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: 0.0001
- 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Roc Auc |
|---|---|---|---|---|
| 0.4787 | 0.17 | 1000 | 0.5094 | 0.7706 |
| 0.3695 | 0.34 | 2000 | 0.5111 | 0.7359 |
| 0.337 | 0.51 | 3000 | 0.4734 | 0.7829 |
| 0.3604 | 0.68 | 4000 | 0.5508 | 0.7457 |
| 0.3222 | 0.85 | 5000 | 0.5817 | 0.7687 |
| 0.2315 | 1.02 | 6000 | 0.6515 | 0.7679 |
| 0.2388 | 1.19 | 7000 | 0.5681 | 0.7543 |
| 0.2691 | 1.36 | 8000 | 0.5307 | 0.7691 |
| 0.268 | 1.53 | 9000 | 0.5643 | 0.7610 |
| 0.131 | 1.7 | 10000 | 0.7293 | 0.7451 |
| 0.2303 | 1.87 | 11000 | 0.6291 | 0.7704 |
| 0.1442 | 2.04 | 12000 | 0.6372 | 0.7871 |
| 0.1325 | 2.21 | 13000 | 0.8672 | 0.7319 |
| 0.1986 | 2.38 | 14000 | 0.7352 | 0.7532 |
| 0.1669 | 2.55 | 15000 | 0.8195 | 0.7562 |
| 0.1228 | 2.72 | 16000 | 1.0106 | 0.7239 |
| 0.1071 | 2.89 | 17000 | 0.8957 | 0.7463 |
| 0.1322 | 3.06 | 18000 | 1.0871 | 0.7408 |
| 0.1676 | 3.24 | 19000 | 0.9173 | 0.7683 |
| 0.1105 | 3.41 | 20000 | 1.0175 | 0.7700 |
| 0.1451 | 3.58 | 21000 | 0.9357 | 0.7404 |
| 0.082 | 3.75 | 22000 | 1.1246 | 0.7404 |
| 0.1457 | 3.92 | 23000 | 1.0082 | 0.7502 |
| 0.0336 | 4.09 | 24000 | 1.3685 | 0.7443 |
| 0.0742 | 4.26 | 25000 | 1.5080 | 0.7227 |
| 0.0353 | 4.43 | 26000 | 1.3573 | 0.7421 |
| 0.0557 | 4.6 | 27000 | 1.2484 | 0.7472 |
| 0.075 | 4.77 | 28000 | 1.2750 | 0.7462 |
| 0.0569 | 4.94 | 29000 | 1.3954 | 0.7355 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
