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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-brain-xray
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: sartajbhuvaji/Brain-Tumor-Classification
type: imagefolder
config: default
split: Testing
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6903553299492385
---
<!-- 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-base-brain-xray
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sartajbhuvaji/Brain-Tumor-Classification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9079
- Accuracy: 0.6904
## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.2478 | 0.5556 | 100 | 0.9079 | 0.6904 |
| 0.1499 | 1.1111 | 200 | 1.1543 | 0.7183 |
| 0.0872 | 1.6667 | 300 | 1.1469 | 0.7614 |
| 0.0118 | 2.2222 | 400 | 1.2361 | 0.7259 |
| 0.0077 | 2.7778 | 500 | 1.2023 | 0.7665 |
| 0.0057 | 3.3333 | 600 | 1.2470 | 0.7640 |
| 0.0053 | 3.8889 | 700 | 1.2096 | 0.7766 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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