bone-fracture-detection-using-x-rays
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0458
- Accuracy: 0.9769
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-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 16
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5407 | 1.0 | 111 | 0.2512 | 0.9143 |
| 0.1819 | 2.0 | 222 | 0.1203 | 0.9526 |
| 0.1351 | 3.0 | 333 | 0.1183 | 0.9521 |
| 0.101 | 4.0 | 444 | 0.0905 | 0.9616 |
| 0.0705 | 5.0 | 555 | 0.0958 | 0.9628 |
| 0.0658 | 6.0 | 666 | 0.0671 | 0.9729 |
| 0.0584 | 7.0 | 777 | 0.0498 | 0.9803 |
| 0.0507 | 8.0 | 888 | 0.0633 | 0.9735 |
| 0.0508 | 9.0 | 999 | 0.0640 | 0.9797 |
| 0.0432 | 10.0 | 1110 | 0.0458 | 0.9769 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2