metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swin-tiny-patch4-window7-224-mulder-v-scully-colab2
    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: 1

swin-tiny-patch4-window7-224-mulder-v-scully-colab2

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: 0.5344
Accuracy: 1.0

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	0.6899	0.5
No log	2.0	2	0.6701	0.25
No log	3.0	3	0.6309	0.5
No log	4.0	4	0.6049	0.5
No log	5.0	5	0.5828	0.5
No log	6.0	6	0.5650	0.75
No log	7.0	7	0.5486	0.75
No log	8.0	8	0.5344	1.0
No log	9.0	9	0.5240	1.0
0.2978	10.0	10	0.5149	1.0
0.2978	11.0	11	0.5066	1.0
0.2978	12.0	12	0.4980	1.0
0.2978	13.0	13	0.4880	1.0
0.2978	14.0	14	0.4699	1.0
0.2978	15.0	15	0.4507	1.0
0.2978	16.0	16	0.4310	1.0
0.2978	17.0	17	0.4155	1.0
0.2978	18.0	18	0.4054	1.0
0.2978	19.0	19	0.3994	1.0
0.1751	20.0	20	0.3970	1.0

Framework versions

Transformers 4.28.0
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3