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essam24
/
vit-brain-tumour-v2

Image Classification
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results
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vit-brain-tumour-v2
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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-brain-tumour-v2
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: essam24/brain-tumour-v2
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8703703703703703
---

<!-- 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-brain-tumour-v2

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 essam24/brain-tumour-v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5359
- Accuracy: 0.8704

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1236        | 0.5128 | 100  | 0.5990          | 0.8481   |
| 0.1695        | 1.0256 | 200  | 0.5359          | 0.8704   |
| 0.0186        | 1.5385 | 300  | 0.5705          | 0.8975   |
| 0.0368        | 2.0513 | 400  | 0.6136          | 0.8975   |
| 0.0036        | 2.5641 | 500  | 0.6122          | 0.9012   |
| 0.0029        | 3.0769 | 600  | 0.6067          | 0.9025   |
| 0.0027        | 3.5897 | 700  | 0.6449          | 0.9025   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1