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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
model-index:
- name: swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_12
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9760408483896308
- name: Precision
type: precision
value: 0.9762470546227865
---
<!-- 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. -->
# swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_12
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0789
- Accuracy: 0.9760
- F1 Score: 0.9761
- Precision: 0.9762
- Sensitivity: 0.9762
- Specificity: 0.9940
## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- 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 | F1 Score | Precision | Sensitivity | Specificity |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:-----------:|:-----------:|
| 0.3644 | 1.0 | 30 | 0.2918 | 0.8955 | 0.8974 | 0.9070 | 0.8957 | 0.9734 |
| 0.2177 | 2.0 | 60 | 0.2319 | 0.9152 | 0.9155 | 0.9237 | 0.9156 | 0.9786 |
| 0.1171 | 3.0 | 90 | 0.1654 | 0.9489 | 0.9494 | 0.9532 | 0.9492 | 0.9872 |
| 0.068 | 4.0 | 120 | 0.1600 | 0.9450 | 0.9451 | 0.9466 | 0.9455 | 0.9861 |
| 0.0499 | 5.0 | 150 | 0.0947 | 0.9654 | 0.9656 | 0.9656 | 0.9657 | 0.9913 |
| 0.0302 | 6.0 | 180 | 0.0882 | 0.9713 | 0.9714 | 0.9715 | 0.9715 | 0.9928 |
| 0.0207 | 7.0 | 210 | 0.1002 | 0.9698 | 0.9699 | 0.9708 | 0.9699 | 0.9924 |
| 0.0205 | 8.0 | 240 | 0.1550 | 0.9525 | 0.9521 | 0.9544 | 0.9529 | 0.9881 |
| 0.0163 | 9.0 | 270 | 0.0789 | 0.9760 | 0.9761 | 0.9762 | 0.9762 | 0.9940 |
| 0.0181 | 10.0 | 300 | 0.0923 | 0.9737 | 0.9737 | 0.9740 | 0.9738 | 0.9934 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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