| | --- |
| | library_name: transformers |
| | license: apache-2.0 |
| | base_model: google/vit-base-patch16-224 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - precision |
| | - recall |
| | - f1 |
| | model-index: |
| | - name: vit-brats-artifact-classifier |
| | 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-brats-artifact-classifier |
| |
|
| | This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.5891 |
| | - Accuracy: 0.8624 |
| | - Precision: 0.8765 |
| | - Recall: 0.8624 |
| | - F1: 0.8638 |
| |
|
| | ## 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: 32 |
| | - eval_batch_size: 32 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 2 |
| | - total_train_batch_size: 64 |
| | - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: cosine |
| | - lr_scheduler_warmup_ratio: 0.1 |
| | - num_epochs: 20 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | | No log | 1.0 | 80 | 0.6962 | 0.6924 | 0.7009 | 0.6924 | 0.6672 | |
| | | 0.8725 | 2.0 | 160 | 0.5648 | 0.7711 | 0.8219 | 0.7711 | 0.7728 | |
| | | 0.8725 | 3.0 | 240 | 0.4637 | 0.8244 | 0.8342 | 0.8244 | 0.8249 | |
| | | 0.3152 | 4.0 | 320 | 0.3919 | 0.8413 | 0.8415 | 0.8413 | 0.8405 | |
| | | 0.3152 | 5.0 | 400 | 0.4121 | 0.8764 | 0.8848 | 0.8764 | 0.8778 | |
| | | 0.1611 | 6.0 | 480 | 0.2989 | 0.8876 | 0.8923 | 0.8876 | 0.8881 | |
| | | 0.1611 | 7.0 | 560 | 0.6423 | 0.8244 | 0.8607 | 0.8244 | 0.8278 | |
| | | 0.1022 | 8.0 | 640 | 0.3978 | 0.8764 | 0.8787 | 0.8764 | 0.8769 | |
| | | 0.1022 | 9.0 | 720 | 0.5891 | 0.8624 | 0.8765 | 0.8624 | 0.8638 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.53.3 |
| | - Pytorch 2.6.0+cu124 |
| | - Datasets 4.4.1 |
| | - Tokenizers 0.21.2 |
| |
|
