metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swin-tiny-patch4-window7-224-finetuned-vosap
    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: 0.75

swin-tiny-patch4-window7-224-finetuned-vosap

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: 0.4894
Accuracy: 0.75

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
No log	1.0	1	0.4894	0.75
No log	2.0	2	0.5365	0.5
No log	3.0	3	0.6957	0.5
No log	4.0	4	0.6781	0.5
No log	5.0	5	0.5617	0.5
No log	6.0	6	0.4461	0.75
No log	7.0	7	0.3368	0.75
No log	8.0	8	0.3289	0.75
No log	9.0	9	0.3642	0.75
0.0539	10.0	10	0.4334	0.75
0.0539	11.0	11	0.5582	0.5
0.0539	12.0	12	0.6676	0.5
0.0539	13.0	13	0.7586	0.5
0.0539	14.0	14	0.7937	0.5
0.0539	15.0	15	0.7986	0.5
0.0539	16.0	16	0.7619	0.5
0.0539	17.0	17	0.7134	0.5
0.0539	18.0	18	0.6725	0.5
0.0539	19.0	19	0.6390	0.5
0.0297	20.0	20	0.6222	0.5

Framework versions

Transformers 4.21.3
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1