Tharsana/vit-base-brain-tumor

clip-brain-tumor

This model uses the Zero-Shot model openai/clip-vit-large-patch14 to classify brain tumors in MRI scans — without any fine-tuning.

The classification is based on the following semantic labels:

“An MRI scan showing a brain tumor”

“An MRI scan showing a healthy brain”

The model was evaluated on an MRI dataset and achieved the following results:

• Accuracy: 0.8770
• Precision: 0.8853
• Recall: 0.8770

vit-base-brain-tumor

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

• Loss: 0.3678
• Accuracy: 0.8699

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.8264	1.0	135	0.6904	0.5709
0.6692	2.0	270	0.5918	0.7313
0.5584	3.0	405	0.5281	0.7836
0.4993	4.0	540	0.4851	0.8321
0.4554	5.0	675	0.4554	0.8321
0.4237	6.0	810	0.4345	0.8246
0.4035	7.0	945	0.4183	0.8246
0.3861	8.0	1080	0.4066	0.8321
0.3793	9.0	1215	0.3976	0.8433
0.3678	10.0	1350	0.3898	0.8433
0.3665	11.0	1485	0.3843	0.8433
0.3564	12.0	1620	0.3802	0.8433
0.3518	13.0	1755	0.3772	0.8470
0.3508	14.0	1890	0.3755	0.8470
0.3518	15.0	2025	0.3750	0.8470

Framework versions

• Transformers 4.50.0
• Pytorch 2.6.0+cu124
• Datasets 3.4.1
• Tokenizers 0.21.1