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library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- brain-tumor
- medical-imaging
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-brain-tumor
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: your-username/brain-tumor
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8698884758364313
---
<!-- 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. -->
# clip-brain-tumor
This model uses the Zero-Shot model openai/clip-vit-large-patch14 to classify brain tumors in MRI scans — without any fine-tuning.
The classification is based on the following semantic labels:
“An MRI scan showing a brain tumor”
“An MRI scan showing a healthy brain”
The model was evaluated on an MRI dataset and achieved the following results:
- Accuracy: 0.8770
- Precision: 0.8853
- Recall: 0.8770
# vit-base-brain-tumor
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the your-username/brain-tumor dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3678
- Accuracy: 0.8699
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8264 | 1.0 | 135 | 0.6904 | 0.5709 |
| 0.6692 | 2.0 | 270 | 0.5918 | 0.7313 |
| 0.5584 | 3.0 | 405 | 0.5281 | 0.7836 |
| 0.4993 | 4.0 | 540 | 0.4851 | 0.8321 |
| 0.4554 | 5.0 | 675 | 0.4554 | 0.8321 |
| 0.4237 | 6.0 | 810 | 0.4345 | 0.8246 |
| 0.4035 | 7.0 | 945 | 0.4183 | 0.8246 |
| 0.3861 | 8.0 | 1080 | 0.4066 | 0.8321 |
| 0.3793 | 9.0 | 1215 | 0.3976 | 0.8433 |
| 0.3678 | 10.0 | 1350 | 0.3898 | 0.8433 |
| 0.3665 | 11.0 | 1485 | 0.3843 | 0.8433 |
| 0.3564 | 12.0 | 1620 | 0.3802 | 0.8433 |
| 0.3518 | 13.0 | 1755 | 0.3772 | 0.8470 |
| 0.3508 | 14.0 | 1890 | 0.3755 | 0.8470 |
| 0.3518 | 15.0 | 2025 | 0.3750 | 0.8470 |
### Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
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