segmentation_model_test

This model is a fine-tuned version of nvidia/mit-b0 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0228
• Mean Iou: 0.4990
• Mean Accuracy: 0.5000
• Overall Accuracy: 0.9977
• Per Category Iou: [0.9976658568580316, 0.00038483740619588223]
• Per Category Accuracy: [0.9996102929714499, 0.00046175157765122367]

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: 6e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Per Category Iou	Per Category Accuracy
0.2991	0.2439	20	0.4820	0.4511	0.5010	0.9002	[0.9002190670776196, 0.0019511332665075433]	[0.9017984806798052, 0.10021548406957057]
0.2151	0.4878	40	0.2171	0.4792	0.5054	0.9560	[0.9559757000209818, 0.0023380252240607505]	[0.9577409321311929, 0.05300908111436047]
0.1517	0.7317	60	0.1387	0.4839	0.5006	0.9659	[0.9658788431968783, 0.0019119587675839745]	[0.9676989446127423, 0.033584731414499]
0.1488	0.9756	80	0.1050	0.4909	0.4991	0.9802	[0.9801804275352807, 0.0015904464567297464]	[0.982060661522954, 0.01622287209481299]
0.0917	1.2195	100	0.0828	0.4924	0.5034	0.9823	[0.9822702579449976, 0.0024875496119523724]	[0.9841420602017377, 0.022718177620440202]
0.0998	1.4634	120	0.0917	0.4915	0.5004	0.9812	[0.981240710481487, 0.0018249920306024864]	[0.9831202984930748, 0.017623518547021703]
0.1005	1.7073	140	0.0599	0.4979	0.4990	0.9956	[0.9955563366152819, 0.0002021822200955648]	[0.9974966613047062, 0.00046175157765122367]
0.0662	1.9512	160	0.0757	0.4970	0.4981	0.9939	[0.9938520901192224, 0.00014614399080267152]	[0.9957890932572371, 0.00046175157765122367]
0.0564	2.1951	180	0.0590	0.4982	0.4993	0.9959	[0.9959318937190048, 0.00037537537537537537]	[0.9978723226736982, 0.0007849776820070802]
0.0506	2.4390	200	0.0426	0.4989	0.4999	0.9974	[0.9974019346864065, 0.00034575755480257246]	[0.9993458564193857, 0.00046175157765122367]
0.0497	2.6829	220	0.0463	0.4983	0.4994	0.9964	[0.9964040866211079, 0.0002498355249460772]	[0.998346063562852, 0.00046175157765122367]
0.0397	2.9268	240	0.0500	0.4984	0.4995	0.9966	[0.9966042672899276, 0.00026455959645842886]	[0.9985466343808574, 0.00046175157765122367]
0.0382	3.1707	260	0.0461	0.4983	0.4994	0.9964	[0.9963626606350271, 0.00024699082840723847]	[0.9983045568381324, 0.00046175157765122367]
0.0369	3.4146	280	0.0407	0.4987	0.4998	0.9972	[0.9971528096493613, 0.0003155138142465004]	[0.9990962458412993, 0.00046175157765122367]
0.0291	3.6585	300	0.0350	0.4987	0.4997	0.9971	[0.9970553103111885, 0.00030507026785169515]	[0.9989985564783473, 0.00046175157765122367]
0.0438	3.9024	320	0.0440	0.4987	0.4998	0.9971	[0.9971233951024839, 0.000312288554624473]	[0.9990667739659018, 0.00046175157765122367]
0.0277	4.1463	340	0.0346	0.4988	0.4999	0.9974	[0.9973527207347574, 0.00033933196846474905]	[0.9992965465504671, 0.00046175157765122367]
0.0277	4.3902	360	0.0302	0.4989	0.5000	0.9975	[0.9975251642646684, 0.0003629676232880027]	[0.999469326170287, 0.00046175157765122367]
0.0255	4.6341	380	0.0330	0.4989	0.4999	0.9975	[0.9974865539276777, 0.00035739388379933527]	[0.9994306405823162, 0.00046175157765122367]
0.0239	4.8780	400	0.0280	0.4990	0.5000	0.9977	[0.9976912875609835, 0.0003890747801727492]	[0.9996357732384688, 0.00046175157765122367]
0.0286	5.1220	420	0.0282	0.4989	0.4999	0.9975	[0.9974798442958035, 0.0003564427018356799]	[0.999423917873468, 0.00046175157765122367]
0.0267	5.3659	440	0.0238	0.4991	0.5001	0.9977	[0.9977252550723469, 0.00039488232506713]	[0.9996698069520132, 0.00046175157765122367]
0.0186	5.6098	460	0.0299	0.4990	0.5000	0.9976	[0.9976214654542921, 0.00037765776653196875]	[0.999565815049516, 0.00046175157765122367]
0.0308	5.8537	480	0.0233	0.4991	0.5001	0.9977	[0.9977203426632961, 0.00039403173268887255]	[0.9996648849687493, 0.00046175157765122367]
0.0303	6.0976	500	0.0238	0.4990	0.5000	0.9976	[0.9976369515242876, 0.0003801317790167258]	[0.9995813313016347, 0.00046175157765122367]
0.028	6.3415	520	0.0241	0.4991	0.5001	0.9978	[0.997765872308157, 0.0004020585397233837]	[0.9997105033502199, 0.00046175157765122367]
0.0168	6.5854	540	0.0262	0.4990	0.5000	0.9976	[0.9976165230915276, 0.0003768749528906309]	[0.999560863054159, 0.00046175157765122367]
0.0186	6.8293	560	0.0268	0.4990	0.5000	0.9976	[0.9976118503121866, 0.0003761378168961107]	[0.9995561811676397, 0.00046175157765122367]
0.026	7.0732	580	0.0283	0.4990	0.5000	0.9976	[0.9975737491883292, 0.00037023324694557573]	[0.9995180057852511, 0.00046175157765122367]
0.0208	7.3171	600	0.0267	0.4990	0.5000	0.9975	[0.9975480489019538, 0.0003663540445486518]	[0.9994922554093947, 0.00046175157765122367]
0.0216	7.5610	620	0.0242	0.4990	0.5001	0.9977	[0.9977005133048106, 0.0003906351727909581]	[0.9996450169631351, 0.00046175157765122367]
0.0246	7.8049	640	0.0243	0.4990	0.5000	0.9976	[0.9975747676145958, 0.00037038866116845276]	[0.9995190261964156, 0.00046175157765122367]
0.0199	8.0488	660	0.0243	0.4991	0.5001	0.9977	[0.9977276813231586, 0.0003953037909633553]	[0.9996722379315521, 0.00046175157765122367]
0.0204	8.2927	680	0.0225	0.4990	0.5000	0.9977	[0.9976660066266002, 0.0003848620910840282]	[0.9996104430319153, 0.00046175157765122367]
0.021	8.5366	700	0.0242	0.4990	0.5000	0.9976	[0.997612030034469, 0.00037616611495636473]	[0.9995563612401981, 0.00046175157765122367]
0.0156	8.7805	720	0.0223	0.4990	0.5000	0.9977	[0.9976639398203532, 0.00038452171906843205]	[0.9996083721974932, 0.00046175157765122367]
0.0226	9.0244	740	0.0218	0.4990	0.5001	0.9977	[0.997695900432897, 0.0003898534151159164]	[0.9996403951008019, 0.00046175157765122367]
0.0204	9.2683	760	0.0228	0.4990	0.5000	0.9977	[0.9976748729258625, 0.0003863291008834059]	[0.9996193266114648, 0.00046175157765122367]
0.022	9.5122	780	0.0222	0.4990	0.5001	0.9977	[0.9976952114974814, 0.0003897369275738876]	[0.9996397048226613, 0.00046175157765122367]
0.0221	9.7561	800	0.0221	0.4990	0.5000	0.9977	[0.9976826608914309, 0.00038762694782541285]	[0.9996271297556637, 0.00046175157765122367]
0.0203	10.0	820	0.0228	0.4990	0.5000	0.9977	[0.9976658568580316, 0.00038483740619588223]	[0.9996102929714499, 0.00046175157765122367]

Framework versions

• Transformers 4.46.3
• Pytorch 2.2.0
• Datasets 2.4.0
• Tokenizers 0.20.3