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-v2
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: essam24/brain-tumour-v2
          type: imagefolder
          config: default
          split: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8703703703703703

vit-brain-tumour-v2

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the essam24/brain-tumour-v2 dataset. It achieves the following results on the evaluation set:

Loss: 0.5359
Accuracy: 0.8704

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
0.1236	0.5128	100	0.5990	0.8481
0.1695	1.0256	200	0.5359	0.8704
0.0186	1.5385	300	0.5705	0.8975
0.0368	2.0513	400	0.6136	0.8975
0.0036	2.5641	500	0.6122	0.9012
0.0029	3.0769	600	0.6067	0.9025
0.0027	3.5897	700	0.6449	0.9025

Framework versions

Transformers 4.42.4
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1