metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swin-tiny-patch4-window7-224-finetuned-main-gpu-20e-final
    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: 0.9913265306122448

swin-tiny-patch4-window7-224-finetuned-main-gpu-20e-final

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: 0.0254
Accuracy: 0.9913

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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5767	1.0	551	0.5565	0.7463
0.3985	2.0	1102	0.3165	0.8711
0.2988	3.0	1653	0.1835	0.9293
0.2449	4.0	2204	0.1150	0.9572
0.2037	5.0	2755	0.0993	0.9632
0.1646	6.0	3306	0.0750	0.9717
0.1995	7.0	3857	0.0610	0.9776
0.1659	8.0	4408	0.0485	0.9815
0.1449	9.0	4959	0.0505	0.9821
0.1315	10.0	5510	0.0444	0.9843
0.102	11.0	6061	0.0440	0.9838
0.1039	12.0	6612	0.0359	0.9870
0.0798	13.0	7163	0.0393	0.9869
0.1033	14.0	7714	0.0343	0.9890
0.078	15.0	8265	0.0298	0.9902
0.0765	16.0	8816	0.0299	0.9901
0.0769	17.0	9367	0.0275	0.9908
0.0751	18.0	9918	0.0271	0.9910
0.0822	19.0	10469	0.0251	0.9917
0.0756	20.0	11020	0.0254	0.9913

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu116
Datasets 2.10.1
Tokenizers 0.13.2