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---
license: apache-2.0
base_model: google/vit-large-patch32-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-large-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: 0.7741116751269036
---
<!-- 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-large-brain-xray
This model is a fine-tuned version of [google/vit-large-patch32-224-in21k](https://huggingface.co/google/vit-large-patch32-224-in21k) on the sartajbhuvaji/Brain-Tumor-Classification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9050
- Accuracy: 0.7741
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.352 | 0.5556 | 100 | 1.2267 | 0.6294 |
| 0.1612 | 1.1111 | 200 | 1.0895 | 0.7538 |
| 0.0473 | 1.6667 | 300 | 0.9050 | 0.7741 |
| 0.0525 | 2.2222 | 400 | 1.0663 | 0.7690 |
| 0.0123 | 2.7778 | 500 | 1.2450 | 0.7462 |
| 0.0066 | 3.3333 | 600 | 1.1283 | 0.7817 |
| 0.0126 | 3.8889 | 700 | 1.1717 | 0.7843 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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