This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the brain-tumor-collection dataset. It achieves the following results on the evaluation set:

Loss: 0.4077
Accuracy: 0.8975

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: 5e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.961	1.0	25	0.7429	0.6825
0.5196	2.0	50	0.4773	0.8725
0.4218	3.0	75	0.4077	0.8975

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0

rochtar/brain_tumors_model

brain_tumors_model

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

rochtar/
brain_tumors_model