metadata

license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
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: 0.808641975308642

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: 0.5712
Accuracy: 0.8086

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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.87	5	1.3767	0.5370
1.289	1.91	11	1.3503	0.5494
1.289	2.96	17	1.3712	0.5556
1.0376	4.0	23	1.3064	0.5556
1.0376	4.87	28	1.1062	0.5802
0.8346	5.91	34	0.9249	0.6481
0.7096	6.96	40	0.8947	0.6235
0.7096	8.0	46	0.8626	0.6543
0.6356	8.87	51	0.6820	0.7222
0.6356	9.91	57	0.7249	0.7346
0.5956	10.96	63	0.6818	0.7407
0.5956	12.0	69	0.6111	0.7840
0.5534	12.87	74	0.6026	0.7778
0.519	13.91	80	0.6070	0.7901
0.519	14.96	86	0.5758	0.7963
0.5117	16.0	92	0.5791	0.7840
0.5117	16.87	97	0.5711	0.8025
0.4913	17.39	100	0.5712	0.8086

Framework versions

Transformers 4.39.3
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2