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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: Brain_Tumor_Classification
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: 0.9646761984861227
- name: F1
type: f1
value: 0.9646761984861227
- name: Recall
type: recall
value: 0.9646761984861227
- name: Precision
type: precision
value: 0.9646761984861227
---
<!-- 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. -->
# Brain_Tumor_Classification
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1012
- Accuracy: 0.9647
- F1: 0.9647
- Recall: 0.9647
- Precision: 0.9647
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.4856 | 0.99 | 83 | 0.3771 | 0.8444 | 0.8444 | 0.8444 | 0.8444 |
| 0.3495 | 1.99 | 166 | 0.2608 | 0.8949 | 0.8949 | 0.8949 | 0.8949 |
| 0.252 | 2.99 | 249 | 0.1445 | 0.9487 | 0.9487 | 0.9487 | 0.9487 |
| 0.2364 | 3.99 | 332 | 0.1029 | 0.9588 | 0.9588 | 0.9588 | 0.9588 |
| 0.2178 | 4.99 | 415 | 0.1012 | 0.9647 | 0.9647 | 0.9647 | 0.9647 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1
|