CoolFace
Modelpublic

Hemg/Wound-Image-classification

sourceHugging Faceapache-2.0updated 3y agoView on Hugging Face
2likes251downloads
Model Card

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

Wound-Image-classification

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: 0.1209
  • Accuracy: 0.965

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
  • trainbatchsize: 4
  • evalbatchsize: 4
  • seed: 42
  • gradientaccumulationsteps: 2
  • totaltrainbatch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lrschedulertype: linear
  • lrschedulerwarmup_ratio: 0.01
  • num_epochs: 16

Training results

Training LossEpochStepValidation LossAccuracy
1.09191.02000.77800.76
0.61572.04000.56950.7925
0.48943.06000.36670.8775
0.37864.08000.44360.8625
0.31425.010000.44120.8625
0.26366.012000.44300.86
0.1987.014000.27600.9175
0.14568.016000.22110.93
0.15869.018000.35200.905
0.130710.020000.31880.9175
0.10611.022000.31670.925
0.097512.024000.26330.92
0.073413.026000.18130.9525
0.099414.028000.21500.945
0.062215.030000.17570.955
0.060916.032000.12090.965

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2