This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.0510
Accuracy: 0.9826
F1: 0.9826
Precision: 0.9828
Recall: 0.9826

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: 0.0001
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: 0.2
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.4479	1.0	95	0.1592	0.9478	0.9478	0.9500	0.9478
0.3078	2.0	190	0.0914	0.9685	0.9686	0.9695	0.9685
0.2307	3.0	285	0.0603	0.9785	0.9785	0.9790	0.9785
0.227	4.0	380	0.0531	0.9811	0.9811	0.9814	0.9811
0.1674	5.0	475	0.0510	0.9826	0.9826	0.9828	0.9826

Framework versions

Transformers 4.22.1
Pytorch 1.12.1+cu113
Datasets 2.5.1
Tokenizers 0.12.1

erikejw/swinv2-tiny-patch4-window8-256-finetuned-eurosat

swinv2-tiny-patch4-window8-256-finetuned-eurosat

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

erikejw/
swinv2-tiny-patch4-window8-256-finetuned-eurosat