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