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

AMfeta99/vit-base-oxford-brain-tumor_try_stuff

vit-base-oxford-brain-tumor_try_stuff

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

AMfeta99/
vit-base-oxford-brain-tumor_try_stuff