metadata

license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
model-index:
  - name: swin-tiny-patch4-window7-224
    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: 0.8933333333333333
          - name: Precision
            type: precision
            value: 0.8772576832151301
          - name: Recall
            type: recall
            value: 0.8933333333333333

swin-tiny-patch4-window7-224

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: 0.2912
Accuracy: 0.8933
Precision: 0.8773
Recall: 0.8933
F1 Score: 0.8762

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: 64
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1 Score
No log	1.0	4	0.4588	0.8708	0.7584	0.8708	0.8107
No log	2.0	8	0.3854	0.8708	0.7584	0.8708	0.8107
No log	3.0	12	0.4070	0.8708	0.7584	0.8708	0.8107
0.4953	4.0	16	0.3890	0.8708	0.7584	0.8708	0.8107
0.4953	5.0	20	0.3688	0.8708	0.7584	0.8708	0.8107
0.4953	6.0	24	0.3549	0.8708	0.7584	0.8708	0.8107
0.4953	7.0	28	0.3138	0.8708	0.7584	0.8708	0.8107
0.4217	8.0	32	0.3330	0.8708	0.8312	0.8708	0.8308
0.4217	9.0	36	0.2946	0.9	0.8881	0.9	0.8845
0.4217	10.0	40	0.2753	0.9042	0.8938	0.9042	0.8905
0.4217	11.0	44	0.2996	0.9	0.8909	0.9	0.8935
0.3747	12.0	48	0.2684	0.9	0.8883	0.9	0.8894
0.3747	13.0	52	0.2670	0.9	0.8883	0.9	0.8894
0.3747	14.0	56	0.2722	0.9042	0.8940	0.9042	0.8951
0.3579	15.0	60	0.2718	0.9042	0.8940	0.9042	0.8951

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3