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mmomm25/
vit-base-patch16-224-in21k-FINALAsphaltLaneClassifier-detectorVIT30epochs

Name: vit-base-patch16-224-in21k-FINALAsphaltLaneClassifier-detectorVIT30epochs
Availability: InStock

tensorboard

safetensors

vit

generated_from_trainer

imagefolder

google/vit-base-patch16-224-in21k

finetune

apache-2.0

model-index

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vit-base-patch16-224-in21k-FINALAsphaltLaneClassifier-detectorVIT30epochs

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.0975
Accuracy: {'accuracy': 0.9566563467492261}
F1: {'f1': 0.9461566578410928}
Precision: {'precision': 0.9423611549883112}
Recall: {'recall': 0.9539001371299508}

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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
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	F1	Precision	Recall
1.5913	0.9907	80	1.5129	{'accuracy': 0.7461300309597523}	{'f1': 0.4885568839223056}	{'precision': 0.4547963454156366}	{'recall': 0.5477280156914024}
0.7749	1.9938	161	0.6719	{'accuracy': 0.9009287925696594}	{'f1': 0.6806448452120003}	{'precision': 0.7905629458261038}	{'recall': 0.7018633540372671}
0.5529	2.9969	242	0.3765	{'accuracy': 0.9318885448916409}	{'f1': 0.7729713140316855}	{'precision': 0.8042461260433723}	{'recall': 0.7677395068699416}
0.3601	4.0	323	0.3341	{'accuracy': 0.9164086687306502}	{'f1': 0.9093567346926615}	{'precision': 0.915458654820357}	{'recall': 0.9270074301130202}
0.3851	4.9907	403	0.2551	{'accuracy': 0.934984520123839}	{'f1': 0.926734220728561}	{'precision': 0.9242424242424241}	{'recall': 0.9466851299149436}
0.2516	5.9938	484	0.1777	{'accuracy': 0.9566563467492261}	{'f1': 0.9489876384049758}	{'precision': 0.9485110663983903}	{'recall': 0.9513860880320507}
0.3202	6.9969	565	0.1609	{'accuracy': 0.9535603715170279}	{'f1': 0.9443998949860868}	{'precision': 0.940001409828996}	{'recall': 0.9518387064970916}
0.1857	8.0	646	0.1253	{'accuracy': 0.9752321981424149}	{'f1': 0.9704532058943071}	{'precision': 0.9726055258065137}	{'recall': 0.9685497387360742}
0.1644	8.9907	726	0.1459	{'accuracy': 0.9628482972136223}	{'f1': 0.9542014027428277}	{'precision': 0.9523602484472049}	{'recall': 0.9575972681562742}
0.2962	9.9938	807	0.1678	{'accuracy': 0.9411764705882353}	{'f1': 0.9353845975481633}	{'precision': 0.9327564716246771}	{'recall': 0.9513233488388769}
0.2872	10.9969	888	0.1710	{'accuracy': 0.9318885448916409}	{'f1': 0.9062805146820121}	{'precision': 0.9236623237302658}	{'recall': 0.9092948114687246}
0.2152	12.0	969	0.1278	{'accuracy': 0.9659442724458205}	{'f1': 0.9592268907563025}	{'precision': 0.9600795718006697}	{'recall': 0.9590268254864528}
0.2789	12.9907	1049	0.1574	{'accuracy': 0.9473684210526315}	{'f1': 0.9401668121351615}	{'precision': 0.9386473340716037}	{'recall': 0.9479712833750101}
0.0852	13.9938	1130	0.1197	{'accuracy': 0.9628482972136223}	{'f1': 0.9543105052140121}	{'precision': 0.9504212454212454}	{'recall': 0.9594794439514935}
0.1408	14.9969	1211	0.0921	{'accuracy': 0.9690402476780186}	{'f1': 0.9595474426584376}	{'precision': 0.9564392324093817}	{'recall': 0.9638084482804979}
0.1505	16.0	1292	0.0999	{'accuracy': 0.9566563467492261}	{'f1': 0.947061703879608}	{'precision': 0.9442258268685393}	{'recall': 0.953062120763984}
0.0824	16.9907	1372	0.1027	{'accuracy': 0.9597523219814241}	{'f1': 0.9507999691104512}	{'precision': 0.9465755000825951}	{'recall': 0.9603936436234574}
0.1285	17.9938	1453	0.1084	{'accuracy': 0.9473684210526315}	{'f1': 0.9384258178429205}	{'precision': 0.9349180559553895}	{'recall': 0.9514264203705197}
0.1324	18.9969	1534	0.1069	{'accuracy': 0.9628482972136223}	{'f1': 0.9542723501653}	{'precision': 0.9523602484472049}	{'recall': 0.9575972681562744}
0.1132	20.0	1615	0.0916	{'accuracy': 0.9566563467492261}	{'f1': 0.9461584792019574}	{'precision': 0.941292743433966}	{'recall': 0.9548412250275603}
0.1222	20.9907	1695	0.1144	{'accuracy': 0.9535603715170279}	{'f1': 0.9435095063666493}	{'precision': 0.9403516555363565}	{'recall': 0.9507945470678391}
0.0937	21.9938	1776	0.1278	{'accuracy': 0.9504643962848297}	{'f1': 0.9421323702425201}	{'precision': 0.9393214628508746}	{'recall': 0.9519148898030886}
0.0806	22.9969	1857	0.0985	{'accuracy': 0.9597523219814241}	{'f1': 0.9496711025800274}	{'precision': 0.9460811144381124}	{'recall': 0.9561677108260959}
0.0916	24.0	1938	0.1051	{'accuracy': 0.9566563467492261}	{'f1': 0.9461566578410928}	{'precision': 0.9423611549883112}	{'recall': 0.9539001371299508}
0.1396	24.9907	2018	0.1085	{'accuracy': 0.9566563467492261}	{'f1': 0.9461566578410928}	{'precision': 0.9423611549883112}	{'recall': 0.9539001371299508}
0.0688	25.9938	2099	0.1062	{'accuracy': 0.9566563467492261}	{'f1': 0.9461566578410928}	{'precision': 0.9423611549883112}	{'recall': 0.9539001371299508}
0.0807	26.9969	2180	0.1021	{'accuracy': 0.9566563467492261}	{'f1': 0.9461566578410928}	{'precision': 0.9423611549883112}	{'recall': 0.9539001371299508}
0.1431	28.0	2261	0.0979	{'accuracy': 0.9566563467492261}	{'f1': 0.9461566578410928}	{'precision': 0.9423611549883112}	{'recall': 0.9539001371299508}
0.092	28.9907	2341	0.0970	{'accuracy': 0.9566563467492261}	{'f1': 0.9461566578410928}	{'precision': 0.9423611549883112}	{'recall': 0.9539001371299508}
0.0881	29.7214	2400	0.0975	{'accuracy': 0.9566563467492261}	{'f1': 0.9461566578410928}	{'precision': 0.9423611549883112}	{'recall': 0.9539001371299508}

Framework versions

Transformers 4.43.3
Pytorch 2.3.1
Datasets 2.20.0
Tokenizers 0.19.1

mmomm25/vit-base-patch16-224-in21k-FINALAsphaltLaneClassifier-detectorVIT30epochs

vit-base-patch16-224-in21k-FINALAsphaltLaneClassifier-detectorVIT30epochs

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mmomm25/
vit-base-patch16-224-in21k-FINALAsphaltLaneClassifier-detectorVIT30epochs