metadata

license: apache-2.0
base_model: microsoft/swin-small-patch4-window7-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swin-small-patch4-window7-224-finetuned-isic217
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.5454545454545454

swin-small-patch4-window7-224-finetuned-isic217

This model is a fine-tuned version of microsoft/swin-small-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 1.9417
Accuracy: 0.5455

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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.1844	0.9796	24	2.1103	0.1364
2.0018	2.0	49	1.8737	0.2727
1.6474	2.9796	73	1.9019	0.2727
1.3757	4.0	98	1.7487	0.3636
1.1526	4.9796	122	1.7576	0.4091
0.9161	6.0	147	1.5886	0.5
0.7568	6.9796	171	1.8935	0.4545
0.4024	8.0	196	1.6767	0.4545
0.814	8.9796	220	1.7112	0.3636
0.4346	10.0	245	1.9364	0.4091
0.3456	10.9796	269	1.9417	0.5455
0.228	12.0	294	2.1569	0.4091
0.1681	12.9796	318	2.0565	0.4545
0.1498	14.0	343	2.0701	0.3636
0.1599	14.9796	367	2.4973	0.5
0.3856	16.0	392	2.2473	0.4545
0.2529	16.9796	416	2.0918	0.4545
0.0557	18.0	441	1.9596	0.5455
0.0895	18.9796	465	2.5522	0.4545
0.0719	20.0	490	2.2938	0.5
0.0764	20.9796	514	2.6754	0.4545
0.1301	22.0	539	2.5287	0.4545
0.1205	22.9796	563	2.7532	0.4091
0.1013	24.0	588	2.6988	0.4545
0.0777	24.9796	612	2.9345	0.4091
0.1807	26.0	637	2.9981	0.4545
0.0298	26.9796	661	2.8549	0.4545
0.0589	28.0	686	2.6967	0.4545
0.0896	28.9796	710	2.6903	0.4545
0.0218	29.3878	720	2.6902	0.4545

Framework versions

Transformers 4.42.0.dev0
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1