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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
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: 0.9396355353075171
- name: Precision
type: precision
value: 0.9408448811333167
---
<!-- 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
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: 0.1577
- Accuracy: 0.9396
- F1 Score: 0.9385
- Precision: 0.9408
## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|
| 1.1562 | 0.99 | 41 | 1.1378 | 0.6378 | 0.6191 | 0.6537 |
| 0.4878 | 1.99 | 82 | 0.6477 | 0.7591 | 0.7499 | 0.7874 |
| 0.2623 | 2.98 | 123 | 0.4410 | 0.8337 | 0.8311 | 0.8488 |
| 0.1985 | 4.0 | 165 | 0.4660 | 0.8144 | 0.8115 | 0.8455 |
| 0.1736 | 4.99 | 206 | 0.3230 | 0.8776 | 0.8760 | 0.8894 |
| 0.124 | 5.99 | 247 | 0.2684 | 0.9026 | 0.9014 | 0.9090 |
| 0.1278 | 6.98 | 288 | 0.2210 | 0.9180 | 0.9166 | 0.9210 |
| 0.0959 | 8.0 | 330 | 0.2151 | 0.9208 | 0.9195 | 0.9260 |
| 0.0849 | 8.99 | 371 | 0.2154 | 0.9220 | 0.9205 | 0.9291 |
| 0.0805 | 9.99 | 412 | 0.2112 | 0.9191 | 0.9179 | 0.9251 |
| 0.0682 | 10.98 | 453 | 0.1563 | 0.9385 | 0.9369 | 0.9402 |
| 0.0624 | 12.0 | 495 | 0.1577 | 0.9396 | 0.9385 | 0.9408 |
| 0.0415 | 12.99 | 536 | 0.1836 | 0.9305 | 0.9294 | 0.9332 |
| 0.0465 | 13.99 | 577 | 0.2145 | 0.9203 | 0.9192 | 0.9252 |
| 0.056 | 14.98 | 618 | 0.1710 | 0.9339 | 0.9325 | 0.9369 |
| 0.0545 | 16.0 | 660 | 0.2094 | 0.9248 | 0.9236 | 0.9298 |
| 0.0591 | 16.99 | 701 | 0.1752 | 0.9317 | 0.9303 | 0.9341 |
| 0.0512 | 17.99 | 742 | 0.1781 | 0.9311 | 0.9297 | 0.9342 |
| 0.0424 | 18.98 | 783 | 0.1873 | 0.9305 | 0.9293 | 0.9338 |
| 0.0438 | 19.88 | 820 | 0.1955 | 0.9265 | 0.9252 | 0.9307 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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