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vri-alzheirmer-classifier

Image Classification
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Safetensors
vit
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vri-alzheirmer-classifier / README.md
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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: []

alz-mri-vit

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

  • Loss: 0.2447
  • F1: 0.9086

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
1.1101 1.0 64 0.9590 0.5549
0.9356 2.0 128 0.8877 0.5940
0.9187 3.0 192 0.9376 0.5272
0.8804 4.0 256 0.8667 0.5962
0.7854 5.0 320 0.7756 0.6720
0.7278 6.0 384 0.7202 0.6860
0.6462 7.0 448 0.7124 0.6898
0.5731 8.0 512 0.6027 0.7553
0.473 9.0 576 0.5520 0.7724
0.4378 10.0 640 0.5550 0.7758
0.4086 11.0 704 0.4366 0.8271
0.36 12.0 768 0.4446 0.8225
0.3217 13.0 832 0.3841 0.8441
0.2941 14.0 896 0.4719 0.8182
0.2679 15.0 960 0.4112 0.8410
0.2565 16.0 1024 0.3698 0.8527
0.2502 17.0 1088 0.3283 0.8810
0.2166 18.0 1152 0.3569 0.8627
0.197 19.0 1216 0.3475 0.8699
0.2004 20.0 1280 0.3171 0.8834
0.1722 21.0 1344 0.2711 0.8998
0.1529 22.0 1408 0.2432 0.9100
0.1495 23.0 1472 0.2950 0.8978
0.1307 24.0 1536 0.2811 0.9034
0.1278 25.0 1600 0.2545 0.9086
0.1175 26.0 1664 0.2561 0.9051
0.1264 27.0 1728 0.2128 0.9186
0.1015 28.0 1792 0.3022 0.9014
0.1077 29.0 1856 0.2403 0.9221
0.0932 30.0 1920 0.2447 0.9086

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.1+cu121
  • Datasets 2.19.2
  • Tokenizers 0.19.1