metadata

license: apache-2.0
base_model: microsoft/swinv2-large-patch4-window12-192-22k
tags:
  - image-classification
  - vision
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: swinv2-large-patch4-window12-192-22k-finetuned-galaxy10-decals
    results: []

swinv2-large-patch4-window12-192-22k-finetuned-galaxy10-decals

This model is a fine-tuned version of microsoft/swinv2-large-patch4-window12-192-22k on the matthieulel/galaxy10_decals dataset. It achieves the following results on the evaluation set:

Loss: 0.4372
Accuracy: 0.8568
Precision: 0.8575
Recall: 0.8568
F1: 0.8550

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
0.974	0.99	62	0.7350	0.7480	0.7464	0.7480	0.7365
0.7716	2.0	125	0.6093	0.7982	0.8102	0.7982	0.7960
0.6813	2.99	187	0.5034	0.8286	0.8301	0.8286	0.8254
0.5998	4.0	250	0.4645	0.8433	0.8431	0.8433	0.8403
0.5306	4.99	312	0.4889	0.8320	0.8377	0.8320	0.8336
0.5234	6.0	375	0.5036	0.8309	0.8398	0.8309	0.8278
0.4984	6.99	437	0.4482	0.8478	0.8484	0.8478	0.8461
0.456	8.0	500	0.4370	0.8557	0.8573	0.8557	0.8557
0.4672	8.99	562	0.4372	0.8568	0.8575	0.8568	0.8550
0.4211	10.0	625	0.4428	0.8523	0.8513	0.8523	0.8505
0.4228	10.99	687	0.4762	0.8433	0.8459	0.8433	0.8435
0.3966	12.0	750	0.4943	0.8410	0.8434	0.8410	0.8404
0.383	12.99	812	0.4885	0.8478	0.8503	0.8478	0.8463
0.3899	14.0	875	0.5021	0.8472	0.8494	0.8472	0.8474
0.3364	14.99	937	0.5107	0.8495	0.8488	0.8495	0.8486
0.331	16.0	1000	0.5219	0.8484	0.8460	0.8484	0.8454
0.288	16.99	1062	0.5696	0.8422	0.8429	0.8422	0.8410
0.2867	18.0	1125	0.5529	0.8484	0.8474	0.8484	0.8473
0.2889	18.99	1187	0.5613	0.8529	0.8522	0.8529	0.8520
0.2809	20.0	1250	0.6093	0.8433	0.8378	0.8433	0.8391
0.2684	20.99	1312	0.6096	0.8444	0.8409	0.8444	0.8419
0.2809	22.0	1375	0.6100	0.8455	0.8453	0.8455	0.8445
0.2661	22.99	1437	0.6161	0.8354	0.8378	0.8354	0.8359
0.2435	24.0	1500	0.6540	0.8517	0.8512	0.8517	0.8512
0.2593	24.99	1562	0.6644	0.8472	0.8462	0.8472	0.8456
0.2343	26.0	1625	0.6655	0.8467	0.8441	0.8467	0.8449
0.2281	26.99	1687	0.6759	0.8450	0.8438	0.8450	0.8440
0.2334	28.0	1750	0.6836	0.8472	0.8445	0.8472	0.8451
0.2129	28.99	1812	0.6731	0.8489	0.8466	0.8489	0.8471
0.2252	29.76	1860	0.6773	0.8467	0.8440	0.8467	0.8449

Framework versions

Transformers 4.37.2
Pytorch 2.3.0
Datasets 2.19.1
Tokenizers 0.15.1