metadata
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: vit-base-oxford-brain-tumor_try_stuff
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: 0.8076923076923077
- name: Precision
type: precision
value: 0.8513986013986015
- name: Recall
type: recall
value: 0.8076923076923077
- name: F1
type: f1
value: 0.7830374753451677
vit-base-oxford-brain-tumor_try_stuff
This model is a fine-tuned version of google/vit-base-patch16-224 on the Mahadih534/brain-tumor-dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.5406
- Accuracy: 0.8077
- Precision: 0.8514
- Recall: 0.8077
- F1: 0.7830
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: 0.0003
- 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.6608 | 1.0 | 11 | 0.5499 | 0.8 | 0.8308 | 0.8 | 0.8039 |
| 0.6097 | 2.0 | 22 | 0.4836 | 0.88 | 0.8989 | 0.88 | 0.8731 |
| 0.5882 | 3.0 | 33 | 0.4191 | 0.88 | 0.8853 | 0.88 | 0.8812 |
| 0.5673 | 4.0 | 44 | 0.4871 | 0.84 | 0.8561 | 0.84 | 0.8427 |
| 0.5619 | 5.0 | 55 | 0.4079 | 0.92 | 0.92 | 0.92 | 0.92 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1