metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - image-classification
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: vit-brain-tumour
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: Simezu/brain-tumour-MRI-scan
          type: imagefolder
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9925442684063374

vit-brain-tumour

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

Loss: 0.0309
Accuracy: 0.9925

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: 0.0002
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.227	0.4255	100	0.3067	0.8910
0.0659	0.8511	200	0.1109	0.9627
0.0404	1.2766	300	0.0900	0.9776
0.05	1.7021	400	0.1082	0.9748
0.006	2.1277	500	0.0374	0.9888
0.0147	2.5532	600	0.0541	0.9888
0.0105	2.9787	700	0.0359	0.9907
0.0032	3.4043	800	0.0392	0.9907
0.0055	3.8298	900	0.0309	0.9925

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.21.0
Tokenizers 0.19.1