transformers tensorboard safetensors vit image-classification generated_from_trainer dataset:imagefolder base_model:google/vit-base-patch16-224-in21k license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: vit-base-patch16-224-in21k_brain_tumor_diagnosis
  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: 0.9857651245551602
    - name: F1
      type: f1
      value: 0.9857500097665184
    - name: Recall
      type: recall
      value: 0.9857651245551602
    - name: Precision
      type: precision
      value: 0.9857741873841454
---

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# vit-base-patch16-224-in21k_brain_tumor_diagnosis

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0630
- Accuracy: 0.9858
- F1: 0.9858
- Recall: 0.9858
- Precision: 0.9858

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 1.379         | 1.0   | 352  | 0.2159          | 0.9310   | 0.9310 | 0.9310 | 0.9390    |
| 0.239         | 2.0   | 704  | 0.0814          | 0.9765   | 0.9766 | 0.9765 | 0.9767    |
| 0.0748        | 3.0   | 1056 | 0.0822          | 0.9808   | 0.9808 | 0.9808 | 0.9812    |
| 0.0748        | 4.0   | 1408 | 0.0651          | 0.9858   | 0.9858 | 0.9858 | 0.9858    |
| 0.0125        | 5.0   | 1760 | 0.0630          | 0.9858   | 0.9858 | 0.9858 | 0.9858    |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
