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vit-base-brain-tumor-detection

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metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: vit-base-brain-tumor-detection
    results: []

vit-base-brain-tumor-detection

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: 0.5832
  • Accuracy: 0.785

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: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.9535 0.4 100 0.8966 0.618
0.862 0.8 200 1.1149 0.561
0.7373 1.2 300 0.8543 0.605
0.6476 1.6 400 0.7307 0.666
0.6712 2.0 500 0.6954 0.694
0.4892 2.4 600 0.6391 0.707
0.5801 2.8 700 0.6247 0.708
0.3505 3.2 800 0.6056 0.778
0.3503 3.6 900 0.6264 0.743
0.3416 4.0 1000 0.5832 0.785
0.1427 4.4 1100 0.7297 0.769
0.1982 4.8 1200 0.7761 0.73
0.193 5.2 1300 0.8467 0.741
0.1831 5.6 1400 0.6975 0.774
0.2612 6.0 1500 0.8719 0.775
0.102 6.4 1600 0.9045 0.788
0.1029 6.8 1700 0.9655 0.783
0.0735 7.2 1800 0.9906 0.78
0.0715 7.6 1900 0.8893 0.787
0.1254 8.0 2000 1.1221 0.761
0.021 8.4 2100 1.1648 0.779
0.0133 8.8 2200 0.9857 0.806
0.0086 9.2 2300 1.0365 0.799
0.0223 9.6 2400 0.9826 0.812
0.0023 10.0 2500 1.0697 0.795
0.0021 10.4 2600 1.0490 0.815
0.0401 10.8 2700 1.1594 0.8
0.0012 11.2 2800 1.0811 0.817
0.0034 11.6 2900 1.0956 0.825
0.0012 12.0 3000 1.2010 0.808
0.0011 12.4 3100 1.1712 0.81
0.0092 12.8 3200 1.1814 0.813
0.0007 13.2 3300 1.1677 0.818
0.0007 13.6 3400 1.1723 0.818
0.0006 14.0 3500 1.1852 0.821
0.0005 14.4 3600 1.1928 0.82
0.0005 14.8 3700 1.2030 0.819
0.0005 15.2 3800 1.2093 0.818
0.0005 15.6 3900 1.2160 0.818
0.0004 16.0 4000 1.2232 0.819
0.0004 16.4 4100 1.2302 0.819
0.0004 16.8 4200 1.2350 0.819
0.0004 17.2 4300 1.2400 0.82
0.0004 17.6 4400 1.2442 0.821
0.0004 18.0 4500 1.2483 0.821
0.0004 18.4 4600 1.2518 0.821
0.0004 18.8 4700 1.2546 0.821
0.0004 19.2 4800 1.2561 0.821
0.0004 19.6 4900 1.2574 0.82
0.0004 20.0 5000 1.2577 0.82

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1