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metadata
license: apache-2.0
base_model: microsoft/swinv2-base-patch4-window12-192-22k
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: Psoriasis-Project-Aug-M2-swinv2-base-patch4-window12-192-22k
    results: []
pipeline_tag: image-classification

Psoriasis-Project-Aug-M2-swinv2-base-patch4-window12-192-22k

This model is a fine-tuned version of microsoft/swinv2-base-patch4-window12-192-22k on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0088
  • Accuracy: 1.0

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: 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: 7

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.7078 0.99 36 0.1447 0.9375
0.1858 1.99 72 0.0584 0.9792
0.0891 2.98 108 0.0082 1.0
0.0619 4.0 145 0.0215 1.0
0.0252 4.99 181 0.0120 1.0
0.018 5.99 217 0.0139 1.0
0.0112 6.95 252 0.0088 1.0

Training results

                 precision    recall  f1-score   support

   Erythromelal       1.00      1.00      1.00         5
        Guttate       1.00      1.00      1.00         7
        Inverse       1.00      1.00      1.00         4
           Nail       1.00      1.00      1.00        10
         Normal       1.00      1.00      1.00        11
         Plaque       1.00      1.00      1.00        10

Psoriatic Arthritis 1.00 1.00 1.00 6

       Pustular       1.00      1.00      1.00         6
       accuracy                           1.00        59
      macro avg       1.00      1.00      1.00        59
   weighted avg       1.00      1.00      1.00        59

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.1.2
  • Datasets 2.18.0
  • Tokenizers 0.15.2