This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on the matthieulel/galaxy10_decals dataset. It achieves the following results on the evaluation set:

Loss: 0.4552
Accuracy: 0.8551
Precision: 0.8529
Recall: 0.8551
F1: 0.8513

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
1.7462	0.99	62	1.4592	0.4431	0.4309	0.4431	0.3967
1.1805	2.0	125	1.0335	0.6460	0.6741	0.6460	0.6241
0.9342	2.99	187	0.7051	0.7537	0.7478	0.7537	0.7394
0.786	4.0	250	0.6468	0.7745	0.7731	0.7745	0.7637
0.7062	4.99	312	0.6013	0.8038	0.8052	0.8038	0.8008
0.7011	6.0	375	0.5373	0.8123	0.8171	0.8123	0.8041
0.7014	6.99	437	0.5470	0.8044	0.8048	0.8044	0.7995
0.6447	8.0	500	0.5309	0.8083	0.8087	0.8083	0.8025
0.608	8.99	562	0.4836	0.8337	0.8323	0.8337	0.8300
0.6196	10.0	625	0.4797	0.8331	0.8293	0.8331	0.8268
0.6031	10.99	687	0.4863	0.8264	0.8274	0.8264	0.8239
0.5462	12.0	750	0.4749	0.8354	0.8341	0.8354	0.8313
0.5868	12.99	812	0.5269	0.8236	0.8268	0.8236	0.8171
0.5844	14.0	875	0.4402	0.8472	0.8447	0.8472	0.8430
0.5326	14.99	937	0.4635	0.8393	0.8359	0.8393	0.8353
0.5313	16.0	1000	0.4734	0.8365	0.8345	0.8365	0.8300
0.4893	16.99	1062	0.4675	0.8365	0.8335	0.8365	0.8316
0.4983	18.0	1125	0.4441	0.8444	0.8431	0.8444	0.8401
0.518	18.99	1187	0.4693	0.8416	0.8441	0.8416	0.8376
0.5228	20.0	1250	0.4732	0.8410	0.8379	0.8410	0.8358
0.4761	20.99	1312	0.4567	0.8489	0.8493	0.8489	0.8460
0.5311	22.0	1375	0.4582	0.8484	0.8469	0.8484	0.8433
0.4894	22.99	1437	0.4627	0.8467	0.8450	0.8467	0.8433
0.4791	24.0	1500	0.4580	0.8506	0.8493	0.8506	0.8481
0.479	24.99	1562	0.4625	0.8472	0.8443	0.8472	0.8433
0.4487	26.0	1625	0.4557	0.8495	0.8469	0.8495	0.8447
0.4515	26.99	1687	0.4501	0.8534	0.8510	0.8534	0.8500
0.4862	28.0	1750	0.4552	0.8551	0.8529	0.8551	0.8513
0.4348	28.99	1812	0.4512	0.8506	0.8486	0.8506	0.8469
0.4623	29.76	1860	0.4539	0.8551	0.8533	0.8551	0.8516

Framework versions

Transformers 4.37.2
Pytorch 2.3.0
Datasets 2.19.1
Tokenizers 0.15.1

matthieulel/swinv2-tiny-patch4-window8-256-finetuned-galaxy10-decals

swinv2-tiny-patch4-window8-256-finetuned-galaxy10-decals

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

matthieulel/
swinv2-tiny-patch4-window8-256-finetuned-galaxy10-decals