metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swin-tiny-patch4-window7-224-finetuned-eurosat
    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: 0.9166666666666666

swin-tiny-patch4-window7-224-finetuned-eurosat

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: 0.2235
Accuracy: 0.9167

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: 2
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 24

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.86	3	0.5572	0.7917
No log	2.0	7	0.4659	0.75
0.4694	2.86	10	0.4492	0.7917
0.4694	4.0	14	0.2875	0.875
0.4694	4.86	17	0.2463	0.875
0.3403	6.0	21	0.2235	0.9167
0.3403	6.86	24	0.2371	0.9167
0.3403	8.0	28	0.1865	0.9167
0.2581	8.86	31	0.3179	0.8333
0.2581	10.0	35	0.2050	0.8333
0.2581	10.86	38	0.2885	0.8333
0.192	12.0	42	0.2371	0.7917
0.192	12.86	45	0.1783	0.875
0.192	14.0	49	0.1164	0.9167
0.1479	14.86	52	0.1250	0.9167
0.1479	16.0	56	0.1491	0.875
0.1479	16.86	59	0.1409	0.875
0.1348	18.0	63	0.1192	0.9167
0.1348	18.86	66	0.1168	0.9167
0.1461	20.0	70	0.1106	0.9167
0.1461	20.57	72	0.1101	0.9167

Framework versions

Transformers 4.27.4
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3