vit-base-patch16-224-drfx-surgery-classifier
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.3696
- Accuracy: 0.875
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: 5e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 4 | 0.5814 | 0.875 |
| No log | 2.0 | 8 | 0.4649 | 0.875 |
| 0.6165 | 3.0 | 12 | 0.4166 | 0.875 |
| 0.6165 | 4.0 | 16 | 0.3897 | 0.875 |
| 0.4482 | 5.0 | 20 | 0.3811 | 0.875 |
| 0.4482 | 6.0 | 24 | 0.3754 | 0.875 |
| 0.4482 | 7.0 | 28 | 0.3724 | 0.875 |
| 0.4023 | 8.0 | 32 | 0.3707 | 0.875 |
| 0.4023 | 9.0 | 36 | 0.3698 | 0.875 |
| 0.4415 | 10.0 | 40 | 0.3696 | 0.875 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3