vit-base-patch16-224-in21k-FINALLaneClassifier-VIT50AUGMENTED

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: 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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.013	1.0	2098	0.0503	{'accuracy': 0.9872512808292625}	{'f1': 0.9872500637993007}	{'precision': 0.9875320438126312}	{'recall': 0.9872891423140888}
0.0202	2.0	4196	0.0034	{'accuracy': 0.9991659716430359}	{'f1': 0.9991659678069054}	{'precision': 0.999164877117633}	{'recall': 0.999168448562604}
0.0007	3.0	6294	0.0340	{'accuracy': 0.9864172524722984}	{'f1': 0.9864157249694355}	{'precision': 0.986738017682643}	{'recall': 0.9864575908766928}
0.0002	4.0	8392	0.0078	{'accuracy': 0.9972596211128322}	{'f1': 0.9972596209572226}	{'precision': 0.9972664606608035}	{'recall': 0.9972677595628415}
0.0001	5.0	10490	0.0051	{'accuracy': 0.9986893840104849}	{'f1': 0.9986893803637995}	{'precision': 0.998688915375447}	{'recall': 0.9986932763126634}
0.0001	6.0	12588	0.0122	{'accuracy': 0.9965447396640057}	{'f1': 0.9965447388791924}	{'precision': 0.9965582720151911}	{'recall': 0.9965550011879306}
0.0002	7.0	14686	0.0019	{'accuracy': 0.999523412367449}	{'f1': 0.9995234093837869}	{'precision': 0.9995224450811844}	{'recall': 0.9995248277500595}
0.0002	8.0	16784	0.0089	{'accuracy': 0.9979745025616585}	{'f1': 0.9979744996862612}	{'precision': 0.9979755665421167}	{'recall': 0.9979798081321687}
0.0413	9.0	18882	0.0082	{'accuracy': 0.9971404742046944}	{'f1': 0.9971404741641006}	{'precision': 0.997148288973384}	{'recall': 0.9971489665003563}
0.0001	10.0	20980	0.0451	{'accuracy': 0.9908256880733946}	{'f1': 0.9908253358952392}	{'precision': 0.9909645623093171}	{'recall': 0.9908529341886434}
0.0	11.0	23078	0.0075	{'accuracy': 0.998212796377934}	{'f1': 0.9982127634963612}	{'precision': 0.998220079886156}	{'recall': 0.9982088765901349}
0.0	12.0	25176	0.0039	{'accuracy': 0.9991659716430359}	{'f1': 0.9991659678069054}	{'precision': 0.999164877117633}	{'recall': 0.999168448562604}
0.013	13.0	27274	0.0107	{'accuracy': 0.997736208745383}	{'f1': 0.9977362066886293}	{'precision': 0.9977383781306159}	{'recall': 0.9977422220071985}
0.0537	14.0	29372	0.0013	{'accuracy': 0.9996425592755868}	{'f1': 0.9996425558453789}	{'precision': 0.9996429388720207}	{'recall': 0.9996422012013773}
0.0018	15.0	31470	0.0115	{'accuracy': 0.9973787680209698}	{'f1': 0.997378766197631}	{'precision': 0.997381574328435}	{'recall': 0.9973851330141593}
0.0049	16.0	33568	0.0040	{'accuracy': 0.9986893840104849}	{'f1': 0.9986893803637995}	{'precision': 0.998688915375447}	{'recall': 0.9986932763126634}
0.0032	17.0	35666	0.0002	{'accuracy': 0.9998808530918623}	{'f1': 0.9998808519484597}	{'precision': 0.9998812351543943}	{'recall': 0.9998804971319312}
0.0002	18.0	37764	0.0018	{'accuracy': 0.9994042654593114}	{'f1': 0.9994042620764765}	{'precision': 0.9994031988541419}	{'recall': 0.9994060346875742}
0.0003	19.0	39862	0.0028	{'accuracy': 0.9986893840104849}	{'f1': 0.9986893803637995}	{'precision': 0.998688915375447}	{'recall': 0.9986932763126634}
0.0	20.0	41960	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	21.0	44058	0.0013	{'accuracy': 0.9996425592755868}	{'f1': 0.9996425568196459}	{'precision': 0.99964174826845}	{'recall': 0.9996436208125445}
0.0005	22.0	46156	0.0032	{'accuracy': 0.9990468247348981}	{'f1': 0.9990468198500874}	{'precision': 0.9990457151585668}	{'recall': 0.9990489456945351}
0.0	23.0	48254	0.0030	{'accuracy': 0.999523412367449}	{'f1': 0.9995234087884033}	{'precision': 0.9995228131486541}	{'recall': 0.9995241179444757}
0.0	24.0	50352	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	25.0	52450	0.0039	{'accuracy': 0.9990468247348981}	{'f1': 0.9990468208243473}	{'precision': 0.9990458015267176}	{'recall': 0.9990496555001188}
0.0	26.0	54548	0.0028	{'accuracy': 0.9992851185511736}	{'f1': 0.9992851148875656}	{'precision': 0.9992840095465394}	{'recall': 0.9992872416250891}
0.0	27.0	56646	0.0010	{'accuracy': 0.9996425592755868}	{'f1': 0.9996425568196459}	{'precision': 0.99964174826845}	{'recall': 0.9996436208125445}
0.0002	28.0	58744	0.0004	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	29.0	60842	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	30.0	62940	0.0018	{'accuracy': 0.999523412367449}	{'f1': 0.9995234093837869}	{'precision': 0.9995224450811844}	{'recall': 0.9995248277500595}
0.0001	31.0	65038	0.0020	{'accuracy': 0.9996425592755868}	{'f1': 0.9996425558453789}	{'precision': 0.9996429388720207}	{'recall': 0.9996422012013773}
0.0002	32.0	67136	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	33.0	69234	0.0014	{'accuracy': 0.9996425592755868}	{'f1': 0.9996425568196459}	{'precision': 0.99964174826845}	{'recall': 0.9996436208125445}
0.0	34.0	71332	0.0110	{'accuracy': 0.9984510901942094}	{'f1': 0.9984510584424513}	{'precision': 0.9984604452865941}	{'recall': 0.9984464627151052}
0.0004	35.0	73430	0.0009	{'accuracy': 0.9998808530918623}	{'f1': 0.9998808521176034}	{'precision': 0.9998805256869773}	{'recall': 0.9998812069375149}
0.0	36.0	75528	0.0009	{'accuracy': 0.9998808530918623}	{'f1': 0.9998808521176034}	{'precision': 0.9998805256869773}	{'recall': 0.9998812069375149}
0.0	37.0	77626	0.0002	{'accuracy': 0.9998808530918623}	{'f1': 0.9998808521176034}	{'precision': 0.9998805256869773}	{'recall': 0.9998812069375149}
0.0	38.0	79724	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	39.0	81822	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	40.0	83920	0.0024	{'accuracy': 0.9994042654593114}	{'f1': 0.999404257847879}	{'precision': 0.999406739439962}	{'recall': 0.9994024856596558}
0.0	41.0	86018	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	42.0	88116	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	43.0	90214	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	44.0	92312	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	45.0	94410	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	46.0	96508	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	47.0	98606	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	48.0	100704	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	49.0	102802	0.0000	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0	50.0	104900	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