---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-brain-tumor-detection
  results: []
---

<!-- 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-tumor-detection

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co./google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5832
- Accuracy: 0.785

## 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: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9535        | 0.4   | 100  | 0.8966          | 0.618    |
| 0.862         | 0.8   | 200  | 1.1149          | 0.561    |
| 0.7373        | 1.2   | 300  | 0.8543          | 0.605    |
| 0.6476        | 1.6   | 400  | 0.7307          | 0.666    |
| 0.6712        | 2.0   | 500  | 0.6954          | 0.694    |
| 0.4892        | 2.4   | 600  | 0.6391          | 0.707    |
| 0.5801        | 2.8   | 700  | 0.6247          | 0.708    |
| 0.3505        | 3.2   | 800  | 0.6056          | 0.778    |
| 0.3503        | 3.6   | 900  | 0.6264          | 0.743    |
| 0.3416        | 4.0   | 1000 | 0.5832          | 0.785    |
| 0.1427        | 4.4   | 1100 | 0.7297          | 0.769    |
| 0.1982        | 4.8   | 1200 | 0.7761          | 0.73     |
| 0.193         | 5.2   | 1300 | 0.8467          | 0.741    |
| 0.1831        | 5.6   | 1400 | 0.6975          | 0.774    |
| 0.2612        | 6.0   | 1500 | 0.8719          | 0.775    |
| 0.102         | 6.4   | 1600 | 0.9045          | 0.788    |
| 0.1029        | 6.8   | 1700 | 0.9655          | 0.783    |
| 0.0735        | 7.2   | 1800 | 0.9906          | 0.78     |
| 0.0715        | 7.6   | 1900 | 0.8893          | 0.787    |
| 0.1254        | 8.0   | 2000 | 1.1221          | 0.761    |
| 0.021         | 8.4   | 2100 | 1.1648          | 0.779    |
| 0.0133        | 8.8   | 2200 | 0.9857          | 0.806    |
| 0.0086        | 9.2   | 2300 | 1.0365          | 0.799    |
| 0.0223        | 9.6   | 2400 | 0.9826          | 0.812    |
| 0.0023        | 10.0  | 2500 | 1.0697          | 0.795    |
| 0.0021        | 10.4  | 2600 | 1.0490          | 0.815    |
| 0.0401        | 10.8  | 2700 | 1.1594          | 0.8      |
| 0.0012        | 11.2  | 2800 | 1.0811          | 0.817    |
| 0.0034        | 11.6  | 2900 | 1.0956          | 0.825    |
| 0.0012        | 12.0  | 3000 | 1.2010          | 0.808    |
| 0.0011        | 12.4  | 3100 | 1.1712          | 0.81     |
| 0.0092        | 12.8  | 3200 | 1.1814          | 0.813    |
| 0.0007        | 13.2  | 3300 | 1.1677          | 0.818    |
| 0.0007        | 13.6  | 3400 | 1.1723          | 0.818    |
| 0.0006        | 14.0  | 3500 | 1.1852          | 0.821    |
| 0.0005        | 14.4  | 3600 | 1.1928          | 0.82     |
| 0.0005        | 14.8  | 3700 | 1.2030          | 0.819    |
| 0.0005        | 15.2  | 3800 | 1.2093          | 0.818    |
| 0.0005        | 15.6  | 3900 | 1.2160          | 0.818    |
| 0.0004        | 16.0  | 4000 | 1.2232          | 0.819    |
| 0.0004        | 16.4  | 4100 | 1.2302          | 0.819    |
| 0.0004        | 16.8  | 4200 | 1.2350          | 0.819    |
| 0.0004        | 17.2  | 4300 | 1.2400          | 0.82     |
| 0.0004        | 17.6  | 4400 | 1.2442          | 0.821    |
| 0.0004        | 18.0  | 4500 | 1.2483          | 0.821    |
| 0.0004        | 18.4  | 4600 | 1.2518          | 0.821    |
| 0.0004        | 18.8  | 4700 | 1.2546          | 0.821    |
| 0.0004        | 19.2  | 4800 | 1.2561          | 0.821    |
| 0.0004        | 19.6  | 4900 | 1.2574          | 0.82     |
| 0.0004        | 20.0  | 5000 | 1.2577          | 0.82     |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1