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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_10
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: 0.9375490966221524
- name: Precision
type: precision
value: 0.9451238954076366
---
<!-- 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_10
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: 0.2175
- Accuracy: 0.9375
- F1 Score: 0.9383
- Precision: 0.9451
- Sensitivity: 0.9381
- Specificity: 0.9843
## 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: 100
- eval_batch_size: 100
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 400
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:-----------:|:-----------:|
| 1.3428 | 0.99 | 19 | 0.7059 | 0.7467 | 0.7535 | 0.7951 | 0.7464 | 0.9332 |
| 0.3308 | 1.97 | 38 | 0.2314 | 0.9183 | 0.9194 | 0.9239 | 0.9191 | 0.9792 |
| 0.1601 | 2.96 | 57 | 0.2024 | 0.9305 | 0.9314 | 0.9349 | 0.9306 | 0.9824 |
| 0.0976 | 4.0 | 77 | 0.3376 | 0.8904 | 0.8943 | 0.9126 | 0.8930 | 0.9724 |
| 0.0585 | 4.99 | 96 | 0.3893 | 0.8830 | 0.8853 | 0.9115 | 0.8854 | 0.9706 |
| 0.0432 | 5.97 | 115 | 0.2559 | 0.9214 | 0.9239 | 0.9330 | 0.9237 | 0.9802 |
| 0.0313 | 6.96 | 134 | 0.2175 | 0.9375 | 0.9383 | 0.9451 | 0.9381 | 0.9843 |
| 0.0176 | 8.0 | 154 | 0.2309 | 0.9313 | 0.9326 | 0.9386 | 0.9320 | 0.9827 |
| 0.0152 | 8.99 | 173 | 0.2358 | 0.9328 | 0.9339 | 0.9416 | 0.9336 | 0.9831 |
| 0.0089 | 9.87 | 190 | 0.2116 | 0.9360 | 0.9374 | 0.9437 | 0.9372 | 0.9839 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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