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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - f1
model-index:
  - name: 7-classifier-finetuned-padchest
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: F1
            type: f1
            value: 0.7449020840597932

7-classifier-finetuned-padchest

This model is a fine-tuned version of nickmuchi/vit-finetuned-chest-xray-pneumonia on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7844
  • F1: 0.7449

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: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss F1
2.0948 1.0 18 1.9801 0.1856
1.916 2.0 36 1.7571 0.3242
1.6873 3.0 54 1.5333 0.4149
1.4576 4.0 72 1.3515 0.4656
1.2824 5.0 90 1.2288 0.4936
1.2004 6.0 108 1.1050 0.5462
1.1264 7.0 126 1.0643 0.5713
1.0149 8.0 144 1.0612 0.5718
0.9839 9.0 162 0.9897 0.6266
0.9001 10.0 180 0.9542 0.6710
0.9093 11.0 198 0.8993 0.6811
0.8824 12.0 216 0.8877 0.7018
0.8237 13.0 234 0.8970 0.7071
0.8446 14.0 252 0.8619 0.7084
0.7766 15.0 270 0.8271 0.7331
0.7405 16.0 288 0.8516 0.7237
0.7672 17.0 306 0.8036 0.7223
0.7149 18.0 324 0.8188 0.7186
0.7 19.0 342 0.8391 0.7274
0.7011 20.0 360 0.7922 0.7424
0.695 21.0 378 0.8065 0.7394
0.6655 22.0 396 0.7783 0.7473
0.6377 23.0 414 0.7977 0.7296
0.6884 24.0 432 0.7724 0.7387
0.614 25.0 450 0.8372 0.7351
0.6008 26.0 468 0.8229 0.7277
0.6402 27.0 486 0.7958 0.7300
0.592 28.0 504 0.8222 0.7264
0.5774 29.0 522 0.7613 0.7511
0.584 30.0 540 0.7866 0.7377
0.558 31.0 558 0.8298 0.7351
0.5871 32.0 576 0.7727 0.7494
0.5608 33.0 594 0.7753 0.7695
0.5385 34.0 612 0.7585 0.7575
0.5461 35.0 630 0.7664 0.7521
0.506 36.0 648 0.7624 0.7581
0.5132 37.0 666 0.7914 0.7347
0.5083 38.0 684 0.7913 0.7425
0.5042 39.0 702 0.7704 0.7556
0.4539 40.0 720 0.7590 0.7578
0.4714 41.0 738 0.7912 0.7503
0.4681 42.0 756 0.7838 0.7420
0.4482 43.0 774 0.7781 0.7345
0.4535 44.0 792 0.7823 0.7415
0.4284 45.0 810 0.8104 0.7449
0.436 46.0 828 0.7829 0.7421
0.4526 47.0 846 0.7932 0.7567
0.4672 48.0 864 0.7827 0.7411
0.4171 49.0 882 0.7835 0.7447
0.4126 50.0 900 0.7844 0.7449

Framework versions

  • Transformers 4.28.0.dev0
  • Pytorch 2.0.0+cu117
  • Datasets 2.18.0
  • Tokenizers 0.13.3