metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
metrics:
  - f1
model-index:
  - name: alz-mri-vit
    results:
      - task:
          name: image-classification
          type: image-classification
        dataset:
          name: Falah/Alzheimer_MRI
          type: Falah/Alzheimer_MRI
          config: default
          split: train
          args: default
        metrics:
          - name: f1
            type: f1
            value: 0.930865
datasets:
  - Falah/Alzheimer_MRI

alz-mri-vit

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on Falah/Alzheimer_MRI dataset (fine-tuning procedure is described here). It achieves the following results on the evaluation set:

Loss: 0.1875
F1: 0.9309

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: 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: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1
1.1218	1.0	64	0.9419	0.5742
0.94	2.0	128	0.9054	0.6029
0.9123	3.0	192	0.9019	0.5262
0.8625	4.0	256	0.8465	0.6029
0.8104	5.0	320	0.7810	0.6319
0.7244	6.0	384	0.7278	0.7037
0.697	7.0	448	0.6300	0.7480
0.5865	8.0	512	0.5659	0.7662
0.5199	9.0	576	0.5445	0.7721
0.4734	10.0	640	0.6750	0.7185
0.4399	11.0	704	0.4893	0.8274
0.3817	12.0	768	0.5578	0.7844
0.3318	13.0	832	0.4699	0.8228
0.3096	14.0	896	0.4460	0.8399
0.2787	15.0	960	0.4105	0.8399
0.2517	16.0	1024	0.3488	0.8578
0.2346	17.0	1088	0.3877	0.8773
0.2286	18.0	1152	0.3420	0.8575
0.1914	19.0	1216	0.4123	0.8682
0.1844	20.0	1280	0.2894	0.8913
0.173	21.0	1344	0.3197	0.8887
0.1687	22.0	1408	0.2626	0.9075
0.1601	23.0	1472	0.2951	0.9068
0.1466	24.0	1536	0.2666	0.9049
0.1468	25.0	1600	0.2136	0.9103
0.1226	26.0	1664	0.2387	0.9127
0.1186	27.0	1728	0.2131	0.9271
0.0951	28.0	1792	0.2520	0.9130
0.1049	29.0	1856	0.2096	0.9259
0.0936	30.0	1920	0.1875	0.9309

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0