ahmedesmail16's picture
Update README.md
fba8e24 verified
metadata
license: apache-2.0
base_model: microsoft/swinv2-base-patch4-window12-192-22k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: Psoriasis-500-100aug-224-swinv2-base-patch4-window12-192-22k
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8200873362445414

Psoriasis-500-100aug-224-swinv2-base-patch4-window12-192-22k

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

  • Loss: 1.1589
  • Accuracy: 0.8201

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: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.5248 0.9973 92 0.7503 0.7694
0.2461 1.9946 184 0.8202 0.7764
0.1164 2.9919 276 0.8260 0.8052
0.0656 4.0 369 1.0366 0.7860
0.0525 4.9973 461 1.0025 0.8148
0.0223 5.9946 553 1.1363 0.7965
0.0022 6.9919 645 1.1911 0.8061
0.009 8.0 738 1.2139 0.7965
0.0073 8.9973 830 1.2066 0.8166
0.0014 9.9729 920 1.1589 0.8201

Classification Report

Class Precision (%) Recall (%) F1-Score (%) Support
Abnormal 68 67 67 108
Erythrodermic 99 75 85 100
Guttate 94 84 89 114
Inverse 88 93 90 108
Nail 88 86 87 99
Normal 84 87 85 82
Not Define 98 99 98 92
Palm Soles 80 80 80 102
Plaque 73 92 81 84
Psoriatic Arthritis 88 75 81 104
Pustular 76 86 80 112
Scalp 86 94 90 80
Accuracy 84 1185
Macro Avg 85 85 84 1185
Weighted Avg 85 84 84 1185

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.1.2
  • Datasets 2.19.2
  • Tokenizers 0.19.1