metadata

license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swin-tiny-patch4-window7-224-finetuned-rsna-2018
    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: 0.7410179640718563

swin-tiny-patch4-window7-224-finetuned-rsna-2018

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

Loss: 0.5745
Accuracy: 0.7410

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: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
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
0.6448	0.9940	83	0.6735	0.6737
0.736	2.0	167	0.6969	0.6557
0.6895	2.9940	250	0.6265	0.6916
0.6631	4.0	334	0.6275	0.7156
0.6725	4.9940	417	0.6311	0.7126
0.6778	6.0	501	0.6194	0.7066
0.6734	6.9940	584	0.6024	0.7141
0.6231	8.0	668	0.6082	0.7231
0.6164	8.9940	751	0.5846	0.7171
0.6261	10.0	835	0.5682	0.7380
0.6153	10.9940	918	0.6007	0.7186
0.6046	12.0	1002	0.5745	0.7410
0.5679	12.9940	1085	0.5957	0.7231
0.6027	14.0	1169	0.5884	0.7216
0.6249	14.9940	1252	0.5808	0.7365
0.6059	16.0	1336	0.5699	0.7350
0.5776	16.9940	1419	0.5770	0.7320
0.5903	18.0	1503	0.5806	0.7216
0.5633	18.9940	1586	0.5768	0.7380
0.5544	20.0	1670	0.5830	0.7350
0.5515	20.9940	1753	0.5966	0.7260
0.5249	22.0	1837	0.6079	0.7335
0.5212	22.9940	1920	0.5972	0.7246
0.5268	24.0	2004	0.5922	0.7231
0.5406	24.9940	2087	0.6100	0.7350
0.5257	26.0	2171	0.6004	0.7305
0.5152	26.9940	2254	0.6092	0.7320
0.4858	28.0	2338	0.6100	0.7231
0.5412	28.9940	2421	0.6116	0.7350
0.4972	29.8204	2490	0.6120	0.7290

Framework versions

Transformers 4.43.3
Pytorch 2.4.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1