File size: 3,534 Bytes
8edb101 fd6fc52 8edb101 8fc6377 8edb101 fd6fc52 8edb101 79b808f e50c9ad 8edb101 fd6fc52 8edb101 79b808f 8edb101 044f8f2 c44555a e675ac1 8edb101 044f8f2 8edb101 c44555a 36496b9 8edb101 36496b9 8edb101 79b808f 8edb101 79b808f 8edb101 79b808f 8edb101 e50c9ad |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 |
---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
- Mahadih534/brain-tumor-dataset
metrics:
- accuracy
model-index:
- name: vit-base-oxford-brain-tumor
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: Mahadih534/brain-tumor-dataset
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6923076923076923
pipeline_tag: image-classification
---
<!-- 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-oxford-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 Mahadih534/brain-tumor-dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5719
- Accuracy: 0.6923
## Model description
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co./google/vit-base-patch16-224), which is a Vision Transformer (ViT)
ViT model is originaly a transformer encoder model pre-trained and fine-tuned on ImageNet 2012.
It was introduced in the paper "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" by Dosovitskiy et al.
The model processes images as sequences of 16x16 patches, adding a [CLS] token for classification tasks, and uses absolute position embeddings. Pre-training enables the model to learn rich image representations, which can be leveraged for downstream tasks by adding a linear classifier on top of the [CLS] token. The weights were converted from the timm repository by Ross Wightman.
## Intended uses & limitations
This must be used for classification of x-ray images of the brain to diagnose of brain tumor.
## Training and evaluation data
The model was fine-tuned in the dataset [Mahadih534/brain-tumor-dataset](https://huggingface.co./datasets/Mahadih534/brain-tumor-dataset) that contains 253 brain images. This dataset was originally created by Yousef Ghanem.
The original dataset was splitted into training and evaluation subsets, 80% for training and 20% for evaluation.
For robust framework evaluation, the evaluation subset is further split into two equal parts for validation and testing.
This results in three distinct datasets: training, validation, and testing
### Training procedure/hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 20
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 11 | 0.5904 | 0.64 |
| No log | 2.0 | 22 | 0.5276 | 0.68 |
| No log | 3.0 | 33 | 0.4864 | 0.8 |
| No log | 4.0 | 44 | 0.4566 | 0.8 |
| No log | 5.0 | 55 | 0.4390 | 0.88 |
| No log | 6.0 | 66 | 0.4294 | 0.96 |
| No log | 7.0 | 77 | 0.4259 | 0.96 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1 |