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ALM-AHME/swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-Lesion-Classification-HAM10000-AH

sourceHugging Faceapache-2.0updated 3y agoView on Hugging Face
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Model Card

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swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-Lesion-Classification-HAM10000-AH

This model is a fine-tuned version of microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft on the imagefolder dataset. It achieves the following results on the evaluation set:

  • —Loss: 0.1143
  • —Accuracy: 0.9681

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-06
  • —trainbatchsize: 16
  • —evalbatchsize: 16
  • —seed: 42
  • —gradientaccumulationsteps: 2
  • —totaltrainbatch_size: 32
  • —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • —lrschedulertype: linear
  • —lrschedulerwarmup_ratio: 0.9
  • —num_epochs: 12

Training results

Training LossEpochStepValidation LossAccuracy
1.95271.01221.97460.1716
1.8182.02441.74230.3628
1.50443.03661.37070.5046
1.11734.04880.97960.6300
0.87145.06100.74750.7379
0.86316.07320.59780.7729
0.6287.08540.47910.8212
0.55888.09760.35170.8705
0.56329.010980.25640.9168
0.369310.012200.18750.9455
0.32111.013420.15250.9424
0.276112.014640.11430.9681

Framework versions

  • —Transformers 4.30.2
  • —Pytorch 2.0.1+cu118
  • —Datasets 2.13.1
  • —Tokenizers 0.13.3