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mmomm25/
vit-base-patch16-224-in21k-FINALLaneClassifier-VIT50epochsAUGMENTED

Name: vit-base-patch16-224-in21k-FINALLaneClassifier-VIT50epochsAUGMENTED
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-FINALLaneClassifier-VIT50epochsAUGMENTED

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: 0.0000
Accuracy: {'accuracy': 1.0}
F1: {'f1': 1.0}
Precision: {'precision': 1.0}
Recall: {'recall': 1.0}

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: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.0297	0.9981	392	0.0204	{'accuracy': 0.9998408150270615}	{'f1': 0.9998407816167802}	{'precision': 0.9998385012919897}	{'recall': 0.9998431126451208}
0.0082	1.9987	785	0.0069	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.008	2.9994	1178	0.0038	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0023	4.0	1571	0.0020	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0035	4.9981	1963	0.0031	{'accuracy': 0.9993632601082458}	{'f1': 0.9993631351350802}	{'precision': 0.9993546305259762}	{'recall': 0.9993724505804833}
0.0011	5.9987	2356	0.0007	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0013	6.9994	2749	0.0005	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0006	8.0	3142	0.0003	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.001	8.9981	3534	0.0002	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0002	9.9987	3927	0.0002	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0004	10.9994	4320	0.0002	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0001	12.0	4713	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0007	12.9981	5105	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0028	13.9987	5498	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0006	14.9994	5891	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0036	16.0	6284	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0016	16.9981	6676	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0026	17.9987	7069	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0007	18.9994	7462	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0011	20.0	7855	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0003	20.9981	8247	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0008	21.9987	8640	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0001	22.9994	9033	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	24.0	9426	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0002	24.9981	9818	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	25.9987	10211	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0002	26.9994	10604	0.0002	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0001	28.0	10997	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	28.9981	11389	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0002	29.9987	11782	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0001	30.9994	12175	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0004	32.0	12568	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	32.9981	12960	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.002	33.9987	13353	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	34.9994	13746	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	36.0	14139	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0001	36.9981	14531	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	37.9987	14924	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	38.9994	15317	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0035	40.0	15710	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0002	40.9981	16102	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	41.9987	16495	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	42.9994	16888	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	44.0	17281	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	44.9981	17673	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	45.9987	18066	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	46.9994	18459	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	48.0	18852	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	48.9981	19244	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	49.9045	19600	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}

Framework versions

Transformers 4.43.3
Pytorch 2.3.1
Datasets 2.20.0
Tokenizers 0.19.1

mmomm25/vit-base-patch16-224-in21k-FINALLaneClassifier-VIT50epochsAUGMENTED

vit-base-patch16-224-in21k-FINALLaneClassifier-VIT50epochsAUGMENTED

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mmomm25/
vit-base-patch16-224-in21k-FINALLaneClassifier-VIT50epochsAUGMENTED