metadata

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

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

This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window16-256 on the matthieulel/galaxy10_decals dataset. It achieves the following results on the evaluation set:

Loss: 0.4595
Accuracy: 0.8551
Precision: 0.8536
Recall: 0.8551
F1: 0.8518

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
1.723	0.99	62	1.4631	0.4803	0.5152	0.4803	0.4359
1.1597	2.0	125	0.9498	0.6759	0.6942	0.6759	0.6657
0.9305	2.99	187	0.6600	0.7728	0.7592	0.7728	0.7620
0.7634	4.0	250	0.6276	0.7875	0.7831	0.7875	0.7765
0.6924	4.99	312	0.5762	0.7943	0.7972	0.7943	0.7934
0.6992	6.0	375	0.5421	0.8123	0.8128	0.8123	0.8059
0.6731	6.99	437	0.5244	0.8129	0.8153	0.8129	0.8108
0.6274	8.0	500	0.5279	0.8055	0.8140	0.8055	0.8019
0.6096	8.99	562	0.4737	0.8354	0.8336	0.8354	0.8321
0.5906	10.0	625	0.4792	0.8382	0.8382	0.8382	0.8357
0.5839	10.99	687	0.5093	0.8224	0.8322	0.8224	0.8199
0.5478	12.0	750	0.4601	0.8433	0.8429	0.8433	0.8411
0.5678	12.99	812	0.5018	0.8269	0.8322	0.8269	0.8233
0.5586	14.0	875	0.4503	0.8439	0.8444	0.8439	0.8423
0.5267	14.99	937	0.4492	0.8444	0.8416	0.8444	0.8424
0.5143	16.0	1000	0.4543	0.8484	0.8458	0.8484	0.8442
0.4608	16.99	1062	0.4616	0.8427	0.8419	0.8427	0.8398
0.4914	18.0	1125	0.4477	0.8501	0.8501	0.8501	0.8479
0.4889	18.99	1187	0.4738	0.8337	0.8383	0.8337	0.8310
0.4943	20.0	1250	0.4758	0.8388	0.8373	0.8388	0.8352
0.4759	20.99	1312	0.4550	0.8478	0.8484	0.8478	0.8456
0.49	22.0	1375	0.4529	0.8512	0.8520	0.8512	0.8489
0.4546	22.99	1437	0.4567	0.8472	0.8456	0.8472	0.8447
0.4638	24.0	1500	0.4598	0.8450	0.8438	0.8450	0.8431
0.4591	24.99	1562	0.4655	0.8529	0.8539	0.8529	0.8507
0.413	26.0	1625	0.4512	0.8546	0.8526	0.8546	0.8514
0.4268	26.99	1687	0.4511	0.8517	0.8506	0.8517	0.8496
0.4497	28.0	1750	0.4595	0.8551	0.8536	0.8551	0.8518
0.4183	28.99	1812	0.4556	0.8540	0.8532	0.8540	0.8512
0.4211	29.76	1860	0.4567	0.8529	0.8523	0.8529	0.8503

Framework versions

Transformers 4.37.2
Pytorch 2.3.0
Datasets 2.19.1
Tokenizers 0.15.1