metadata

license: apache-2.0
base_model: microsoft/swinv2-small-patch4-window16-256
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: swinv2-small-patch4-window16-256-finetuned-galaxy10-decals
    results: []

swinv2-small-patch4-window16-256-finetuned-galaxy10-decals

This model is a fine-tuned version of microsoft/swinv2-small-patch4-window16-256 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.4737
Accuracy: 0.8489
Precision: 0.8486
Recall: 0.8489
F1: 0.8472

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: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.6168	0.99	62	1.3397	0.5006	0.4880	0.5006	0.4599
0.9396	2.0	125	0.7823	0.7463	0.7602	0.7463	0.7410
0.782	2.99	187	0.5995	0.7948	0.7937	0.7948	0.7885
0.6373	4.0	250	0.5227	0.8230	0.8192	0.8230	0.8176
0.6047	4.99	312	0.5238	0.8281	0.8272	0.8281	0.8262
0.6143	6.0	375	0.5091	0.8348	0.8429	0.8348	0.8298
0.5805	6.99	437	0.4921	0.8264	0.8275	0.8264	0.8254
0.5476	8.0	500	0.4832	0.8320	0.8409	0.8320	0.8291
0.5333	8.99	562	0.4456	0.8501	0.8500	0.8501	0.8477
0.5062	10.0	625	0.4493	0.8467	0.8480	0.8467	0.8457
0.5001	10.99	687	0.4617	0.8450	0.8468	0.8450	0.8449
0.4572	12.0	750	0.4497	0.8467	0.8450	0.8467	0.8449
0.4681	12.99	812	0.4588	0.8489	0.8486	0.8489	0.8452
0.4747	14.0	875	0.4281	0.8529	0.8554	0.8529	0.8508
0.4283	14.99	937	0.4406	0.8602	0.8577	0.8602	0.8585
0.4296	16.0	1000	0.4458	0.8534	0.8512	0.8534	0.8498
0.3734	16.99	1062	0.4623	0.8416	0.8419	0.8416	0.8386
0.3921	18.0	1125	0.4438	0.8517	0.8506	0.8517	0.8496
0.3954	18.99	1187	0.4712	0.8467	0.8487	0.8467	0.8446
0.3995	20.0	1250	0.4648	0.8484	0.8467	0.8484	0.8448
0.3859	20.99	1312	0.4728	0.8495	0.8487	0.8495	0.8462
0.4046	22.0	1375	0.4720	0.8472	0.8467	0.8472	0.8453
0.3651	22.99	1437	0.4837	0.8416	0.8409	0.8416	0.8396
0.3481	24.0	1500	0.4742	0.8540	0.8522	0.8540	0.8524
0.3706	24.99	1562	0.4846	0.8478	0.8477	0.8478	0.8455
0.3278	26.0	1625	0.4798	0.8506	0.8502	0.8506	0.8484
0.3484	26.99	1687	0.4675	0.8529	0.8538	0.8529	0.8520
0.3626	28.0	1750	0.4768	0.8450	0.8446	0.8450	0.8429
0.3324	28.99	1812	0.4725	0.8484	0.8470	0.8484	0.8460
0.3462	29.76	1860	0.4737	0.8489	0.8486	0.8489	0.8472

Framework versions

Transformers 4.37.2
Pytorch 2.3.0
Datasets 2.19.1
Tokenizers 0.15.1