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wang1215/
segformer-b3

Name: segformer-b3
Availability: InStock

generated_from_trainer

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segformer-b3

This model is a fine-tuned version of nvidia/mit-b3 on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set:

Loss: 0.7826
Mean Iou: 0.3995
Mean Accuracy: 0.4977
Overall Accuracy: 0.8759
Accuracy Unlabeled: nan
Accuracy Flat-road: 0.9069
Accuracy Flat-sidewalk: 0.9471
Accuracy Flat-crosswalk: 0.5043
Accuracy Flat-cyclinglane: 0.8684
Accuracy Flat-parkingdriveway: 0.5057
Accuracy Flat-railtrack: 0.0
Accuracy Flat-curb: 0.7351
Accuracy Human-person: 0.8662
Accuracy Human-rider: 0.2599
Accuracy Vehicle-car: 0.9494
Accuracy Vehicle-truck: 0.1607
Accuracy Vehicle-bus: 0.0044
Accuracy Vehicle-tramtrain: 0.1992
Accuracy Vehicle-motorcycle: 0.0
Accuracy Vehicle-bicycle: 0.7913
Accuracy Vehicle-caravan: 0.4628
Accuracy Vehicle-cartrailer: 0.0106
Accuracy Construction-building: 0.9117
Accuracy Construction-door: 0.2679
Accuracy Construction-wall: 0.6351
Accuracy Construction-fenceguardrail: 0.5893
Accuracy Construction-bridge: 0.5639
Accuracy Construction-tunnel: nan
Accuracy Construction-stairs: 0.4246
Accuracy Object-pole: 0.6323
Accuracy Object-trafficsign: 0.4266
Accuracy Object-trafficlight: 0.2431
Accuracy Nature-vegetation: 0.9540
Accuracy Nature-terrain: 0.8819
Accuracy Sky: 0.9827
Accuracy Void-ground: 0.0045
Accuracy Void-dynamic: 0.2006
Accuracy Void-static: 0.5328
Accuracy Void-unclear: 0.0
Iou Unlabeled: 0.0
Iou Flat-road: 0.7947
Iou Flat-sidewalk: 0.8656
Iou Flat-crosswalk: 0.4529
Iou Flat-cyclinglane: 0.6876
Iou Flat-parkingdriveway: 0.4461
Iou Flat-railtrack: 0.0
Iou Flat-curb: 0.5989
Iou Human-person: 0.6127
Iou Human-rider: 0.2346
Iou Vehicle-car: 0.8877
Iou Vehicle-truck: 0.0662
Iou Vehicle-bus: 0.0044
Iou Vehicle-tramtrain: 0.1985
Iou Vehicle-motorcycle: 0.0
Iou Vehicle-bicycle: 0.5765
Iou Vehicle-caravan: 0.1495
Iou Vehicle-cartrailer: 0.0106
Iou Construction-building: 0.8060
Iou Construction-door: 0.2190
Iou Construction-wall: 0.5015
Iou Construction-fenceguardrail: 0.4923
Iou Construction-bridge: 0.3467
Iou Construction-tunnel: nan
Iou Construction-stairs: 0.3908
Iou Object-pole: 0.4693
Iou Object-trafficsign: 0.3698
Iou Object-trafficlight: 0.2052
Iou Nature-vegetation: 0.8832
Iou Nature-terrain: 0.7906
Iou Sky: 0.9519
Iou Void-ground: 0.0038
Iou Void-dynamic: 0.1774
Iou Void-static: 0.3885
Iou Void-unclear: 0.0

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: 6e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Unlabeled	Accuracy Flat-road	Accuracy Flat-sidewalk	Accuracy Flat-crosswalk	Accuracy Flat-cyclinglane	Accuracy Flat-parkingdriveway	Accuracy Flat-curb	Accuracy Human-person	Accuracy Human-rider	Accuracy Vehicle-car	Accuracy Vehicle-truck	Accuracy Vehicle-bus	Accuracy Vehicle-tramtrain	Accuracy Vehicle-motorcycle	Accuracy Vehicle-bicycle	Accuracy Vehicle-caravan	Accuracy Vehicle-cartrailer	Accuracy Construction-building	Accuracy Construction-door	Accuracy Construction-wall	Accuracy Construction-fenceguardrail	Accuracy Construction-bridge	Accuracy Construction-tunnel	Accuracy Construction-stairs	Accuracy Object-pole	Accuracy Object-trafficsign	Accuracy Object-trafficlight	Accuracy Nature-vegetation	Accuracy Nature-terrain	Accuracy Sky	Accuracy Void-ground	Accuracy Void-dynamic	Accuracy Void-static	Accuracy Void-unclear	Iou Unlabeled	Iou Flat-road	Iou Flat-sidewalk	Iou Flat-crosswalk	Iou Flat-cyclinglane	Iou Flat-parkingdriveway	Iou Flat-curb	Iou Human-person	Iou Human-rider	Iou Vehicle-car	Iou Vehicle-truck	Iou Vehicle-bus	Iou Vehicle-tramtrain	Iou Vehicle-motorcycle	Iou Vehicle-bicycle	Iou Vehicle-caravan	Iou Vehicle-cartrailer	Iou Construction-building	Iou Construction-door	Iou Construction-wall	Iou Construction-fenceguardrail	Iou Construction-bridge	Iou Construction-tunnel	Iou Construction-stairs	Iou Object-pole	Iou Object-trafficsign	Iou Object-trafficlight	Iou Nature-vegetation	Iou Nature-terrain	Iou Sky	Iou Void-ground	Iou Void-dynamic	Iou Void-static	Iou Void-unclear
0.7275	2.5	500	0.5765	0.3050	0.3654	0.8441	nan	0.9561	0.9153	0.3719	0.7164	0.4360	0.3475	0.8270	0.0	0.9318	0.0	0.0	0.0	0.0	0.6843	0.0	0.0	0.9160	0.0667	0.3893	0.6512	0.0	nan	0.0	0.5447	0.0525	0.0	0.9581	0.8185	0.9737	0.0	0.0262	0.4752	0.0	nan	0.7208	0.8407	0.3582	0.6393	0.3693	0.2705	0.5291	0.0	0.8548	0.0	0.0	0.0	0.0	0.5158	0.0	0.0	0.7684	0.0638	0.3606	0.4620	0.0	nan	0.0	0.3805	0.0522	0.0	0.8572	0.7657	0.9225	0.0	0.0256	0.3078	0.0
0.3654	5.0	1000	0.5265	0.3531	0.4276	0.8622	nan	0.9116	0.9476	0.4986	0.8194	0.4632	0.5613	0.8672	0.1407	0.9399	0.2129	0.0	0.0	0.0	0.7640	0.0	0.0	0.8915	0.1468	0.5813	0.5719	0.0	nan	0.3336	0.5583	0.4068	0.0	0.9470	0.8512	0.9780	0.0004	0.1935	0.5228	0.0	nan	0.7904	0.8491	0.4455	0.6935	0.4013	0.4607	0.5435	0.1227	0.8663	0.1019	0.0	0.0	0.0	0.5464	0.0	0.0	0.7740	0.1198	0.4746	0.4587	0.0	nan	0.2403	0.4036	0.2949	0.0	0.8663	0.7614	0.9334	0.0003	0.1492	0.3544	0.0
0.2359	7.5	1500	0.5790	0.3584	0.4296	0.8649	nan	0.8646	0.9501	0.4466	0.8506	0.5513	0.7099	0.8317	0.2099	0.9442	0.2546	0.0	0.0	0.0	0.7862	0.0087	0.0	0.9079	0.1046	0.6479	0.5239	0.0	nan	0.1543	0.5674	0.3864	0.0	0.9461	0.8753	0.9776	0.0000	0.1974	0.4793	0.0	nan	0.7917	0.8414	0.4214	0.7012	0.4494	0.5333	0.5888	0.1897	0.8663	0.1009	0.0	0.0	0.0	0.5427	0.0042	0.0	0.7817	0.0945	0.4696	0.4312	0.0	nan	0.1477	0.4314	0.3152	0.0	0.8749	0.7719	0.9409	0.0000	0.1780	0.3593	0.0
0.1708	10.0	2000	0.6066	0.3684	0.4479	0.8666	nan	0.8819	0.9466	0.5609	0.8324	0.4835	0.7200	0.8575	0.1404	0.9422	0.2656	0.0	0.0590	0.0	0.7505	0.2619	0.0	0.8906	0.2203	0.6425	0.5323	0.0	nan	0.3455	0.5923	0.4085	0.0	0.9552	0.8844	0.9791	0.0024	0.0951	0.5293	0.0	nan	0.7930	0.8537	0.4562	0.6519	0.4311	0.5478	0.5960	0.1301	0.8728	0.1051	0.0	0.0590	0.0	0.5390	0.0916	0.0	0.7864	0.1779	0.4949	0.4467	0.0	nan	0.3107	0.4336	0.3289	0.0	0.8757	0.7694	0.9416	0.0019	0.0893	0.3741	0.0
0.1326	12.5	2500	0.5934	0.3969	0.4877	0.8753	nan	0.9227	0.9490	0.4762	0.8499	0.5255	0.6941	0.8115	0.3960	0.9430	0.3828	0.0266	0.0998	0.0	0.7963	0.6565	0.0003	0.8988	0.2880	0.6352	0.5442	0.2746	nan	0.3048	0.6133	0.4269	0.0	0.9483	0.9064	0.9838	0.0025	0.2297	0.5070	0.0	nan	0.8032	0.8631	0.4521	0.7678	0.4440	0.5593	0.6096	0.2994	0.8777	0.1308	0.0266	0.0998	0.0	0.5663	0.2016	0.0003	0.7931	0.1957	0.4838	0.4475	0.2094	nan	0.2849	0.4530	0.3424	0.0	0.8798	0.7777	0.9441	0.0023	0.1979	0.3857	0.0
0.1116	15.0	3000	0.6267	0.3978	0.4820	0.8734	nan	0.9155	0.9431	0.5145	0.8423	0.4973	0.7284	0.8513	0.3146	0.9492	0.0753	0.0	0.2565	0.0	0.7845	0.3853	0.0220	0.8998	0.2497	0.6306	0.5571	0.3186	nan	0.4912	0.5902	0.4795	0.0	0.9501	0.9004	0.9843	0.0038	0.2017	0.5677	0.0	nan	0.8037	0.8591	0.4665	0.7147	0.4311	0.5698	0.5996	0.2693	0.8800	0.0366	0.0	0.2554	0.0	0.5526	0.1200	0.0216	0.7935	0.1949	0.4853	0.4704	0.2116	nan	0.3980	0.4450	0.3691	0.0	0.8815	0.7857	0.9439	0.0031	0.1751	0.3899	0.0
0.098	17.5	3500	0.6334	0.3922	0.5006	0.8729	nan	0.8961	0.9419	0.5747	0.8862	0.4977	0.7428	0.8491	0.3477	0.9464	0.0952	0.0	0.2937	0.0	0.7908	0.7738	0.0	0.8934	0.2479	0.6445	0.6108	0.4273	nan	0.4435	0.6190	0.4308	0.0015	0.9486	0.9026	0.9818	0.0099	0.2216	0.4994	0.0	0.0	0.7961	0.8651	0.5005	0.6765	0.4413	0.5751	0.6176	0.2944	0.8811	0.0373	0.0	0.2919	0.0	0.5578	0.2307	0.0	0.7961	0.1835	0.4901	0.4814	0.2506	nan	0.3771	0.4560	0.3562	0.0015	0.8810	0.7806	0.9474	0.0077	0.1823	0.3784	0.0
0.0894	20.0	4000	0.6973	0.3988	0.4923	0.8722	nan	0.8952	0.9456	0.5309	0.8357	0.4777	0.7630	0.8291	0.2785	0.9467	0.1712	0.0047	0.1377	0.0	0.7854	0.8237	0.0	0.9282	0.1899	0.5904	0.6020	0.4761	nan	0.3323	0.6192	0.4047	0.1381	0.9522	0.8851	0.9767	0.0050	0.1916	0.5277	0.0	nan	0.8062	0.8543	0.4692	0.6675	0.4204	0.5710	0.6083	0.2366	0.8855	0.0677	0.0046	0.1374	0.0	0.5492	0.2453	0.0	0.7996	0.1571	0.4887	0.4847	0.2656	nan	0.3148	0.4670	0.3491	0.1264	0.8815	0.7829	0.9496	0.0043	0.1718	0.3936	0.0
0.0819	22.5	4500	0.6867	0.4098	0.5001	0.8778	nan	0.9344	0.9410	0.5690	0.8783	0.4856	0.7065	0.8495	0.2085	0.9415	0.1530	0.0018	0.2354	0.0	0.7829	0.7796	0.0	0.9044	0.2261	0.6171	0.6045	0.4780	nan	0.4156	0.6265	0.4288	0.1457	0.9563	0.8877	0.9804	0.0064	0.2136	0.5447	0.0	nan	0.8016	0.8702	0.4902	0.7597	0.4279	0.5780	0.6123	0.1998	0.8889	0.0577	0.0018	0.2348	0.0	0.5898	0.2436	0.0	0.7992	0.1842	0.4829	0.4918	0.2855	nan	0.3732	0.4658	0.3650	0.1297	0.8823	0.7837	0.9500	0.0053	0.1841	0.3839	0.0
0.0767	25.0	5000	0.7377	0.4096	0.5109	0.8720	nan	0.8599	0.9464	0.5724	0.9354	0.4838	0.7392	0.8475	0.2679	0.9530	0.2438	0.0	0.2405	0.0	0.7879	0.8364	0.0	0.9155	0.2107	0.5924	0.5901	0.5525	nan	0.3980	0.6229	0.4648	0.2165	0.9550	0.8865	0.9823	0.0047	0.1970	0.5557	0.0	nan	0.7881	0.8643	0.5042	0.6317	0.4280	0.5817	0.6075	0.2397	0.8857	0.1052	0.0	0.2384	0.0	0.5664	0.2501	0.0	0.8056	0.1813	0.4878	0.4863	0.2871	nan	0.3652	0.4725	0.3883	0.1660	0.8804	0.7905	0.9503	0.0040	0.1704	0.3891	0.0
0.0725	27.5	5500	0.7085	0.3977	0.5056	0.8782	nan	0.9177	0.9482	0.4916	0.8966	0.4989	0.7119	0.8469	0.2483	0.9512	0.2387	0.0440	0.1287	0.0	0.7947	0.8184	0.0	0.9152	0.2257	0.6472	0.5963	0.5426	nan	0.3951	0.6422	0.4369	0.2195	0.9499	0.8824	0.9821	0.0036	0.1824	0.5266	0.0	0.0	0.8109	0.8638	0.4498	0.7314	0.4437	0.5797	0.6047	0.2215	0.8861	0.0855	0.0430	0.1284	0.0	0.5657	0.2395	0.0	0.8058	0.1939	0.5113	0.4913	0.2943	nan	0.3732	0.4773	0.3770	0.1643	0.8836	0.7864	0.9509	0.0029	0.1639	0.3905	0.0
0.0685	30.0	6000	0.7388	0.4115	0.5051	0.8738	nan	0.9135	0.9420	0.5290	0.8405	0.4909	0.7408	0.8566	0.3161	0.9461	0.1138	0.0003	0.1616	0.0	0.8061	0.7486	0.0001	0.9074	0.2986	0.6418	0.5669	0.4769	nan	0.4607	0.6454	0.4717	0.2320	0.9531	0.8849	0.9802	0.0037	0.1983	0.5417	0.0	nan	0.7911	0.8647	0.4671	0.6651	0.4361	0.5848	0.6127	0.2642	0.8885	0.0453	0.0003	0.1613	0.0	0.5455	0.2421	0.0001	0.8022	0.2382	0.4975	0.4741	0.3279	nan	0.4050	0.4789	0.3937	0.1921	0.8825	0.7873	0.9516	0.0032	0.1809	0.3970	0.0
0.0654	32.5	6500	0.7246	0.4128	0.5034	0.8789	nan	0.9247	0.9424	0.5865	0.8579	0.5105	0.7409	0.8799	0.2449	0.9462	0.0922	0.0	0.1728	0.0	0.7762	0.7085	0.0	0.9151	0.2459	0.6278	0.6088	0.5426	nan	0.4260	0.6444	0.4471	0.2230	0.9530	0.8839	0.9833	0.0040	0.1978	0.5251	0.0	nan	0.8010	0.8705	0.5132	0.7193	0.4466	0.5906	0.5971	0.2204	0.8884	0.0419	0.0	0.1724	0.0	0.5623	0.2184	0.0	0.8044	0.2015	0.5037	0.4964	0.3206	nan	0.4032	0.4828	0.3859	0.1802	0.8828	0.7909	0.9505	0.0033	0.1792	0.3959	0.0
0.0629	35.0	7000	0.7655	0.4168	0.5105	0.8741	nan	0.8961	0.9470	0.5214	0.8906	0.4982	0.7542	0.8631	0.2754	0.9512	0.1882	0.0015	0.3457	0.0	0.7778	0.6418	0.0144	0.8908	0.2816	0.6612	0.5910	0.5330	nan	0.4434	0.6305	0.4273	0.2421	0.9516	0.8805	0.9821	0.0036	0.2172	0.5444	0.0	nan	0.7981	0.8672	0.4665	0.6765	0.4364	0.5934	0.6114	0.2489	0.8877	0.0831	0.0013	0.3436	0.0	0.5668	0.2017	0.0140	0.7928	0.2283	0.4708	0.4904	0.3458	nan	0.4011	0.4722	0.3699	0.1843	0.8836	0.7898	0.9516	0.0030	0.1851	0.3897	0.0
0.0607	37.5	7500	0.7668	0.4180	0.5139	0.8751	nan	0.8948	0.9480	0.5612	0.8579	0.4903	0.7432	0.8676	0.2619	0.9495	0.1718	0.0165	0.3359	0.0010	0.7738	0.7077	0.0304	0.9104	0.2826	0.6353	0.6045	0.5609	nan	0.4406	0.6293	0.4355	0.2376	0.9511	0.8940	0.9818	0.0033	0.2277	0.5530	0.0	nan	0.7933	0.8676	0.4914	0.6562	0.4327	0.5956	0.6059	0.2348	0.8875	0.0739	0.0161	0.3343	0.0010	0.5696	0.2086	0.0295	0.8084	0.2268	0.5014	0.4962	0.3297	nan	0.3948	0.4702	0.3754	0.1918	0.8836	0.7857	0.9519	0.0029	0.1886	0.3900	0.0
0.0582	40.0	8000	0.7562	0.4049	0.5074	0.8780	nan	0.9204	0.9463	0.5052	0.8643	0.5082	0.7360	0.8650	0.2462	0.9501	0.1893	0.0024	0.2403	0.0	0.7814	0.6631	0.0242	0.9169	0.2821	0.6356	0.5984	0.5609	nan	0.4218	0.6299	0.4414	0.2421	0.9504	0.8835	0.9796	0.0043	0.2138	0.5395	0.0	0.0	0.8004	0.8683	0.4553	0.7073	0.4478	0.6007	0.6132	0.2291	0.8882	0.0839	0.0024	0.2391	0.0	0.5777	0.2020	0.0237	0.8060	0.2276	0.5058	0.4990	0.3415	nan	0.3905	0.4699	0.3815	0.1952	0.8837	0.7908	0.9522	0.0036	0.1849	0.3941	0.0
0.0565	42.5	8500	0.7834	0.4004	0.5024	0.8762	nan	0.8960	0.9474	0.5430	0.8894	0.4937	0.7492	0.8696	0.2727	0.9482	0.1505	0.0006	0.1868	0.0	0.7945	0.5042	0.0051	0.9155	0.2834	0.6369	0.5958	0.5811	nan	0.4136	0.6419	0.4457	0.2481	0.9510	0.8887	0.9822	0.0041	0.2054	0.5358	0.0	0.0	0.7955	0.8676	0.4613	0.6877	0.4390	0.6023	0.6092	0.2503	0.8879	0.0626	0.0006	0.1860	0.0	0.5802	0.1628	0.0051	0.8061	0.2307	0.5003	0.4961	0.3290	nan	0.3820	0.4752	0.3819	0.2070	0.8838	0.7914	0.9520	0.0034	0.1813	0.3944	0.0
0.0562	45.0	9000	0.7812	0.4015	0.5008	0.8772	nan	0.9064	0.9466	0.5058	0.8872	0.5059	0.7482	0.8642	0.2957	0.9489	0.1494	0.0059	0.1674	0.0003	0.8079	0.4484	0.0122	0.9134	0.2785	0.6303	0.6007	0.5723	nan	0.4337	0.6286	0.4291	0.2541	0.9521	0.8902	0.9821	0.0054	0.2097	0.5444	0.0	0.0	0.7954	0.8685	0.4597	0.7046	0.4471	0.6024	0.6174	0.2611	0.8881	0.0630	0.0057	0.1667	0.0003	0.5844	0.1441	0.0120	0.8059	0.2279	0.5021	0.4963	0.3439	nan	0.3981	0.4694	0.3714	0.2134	0.8841	0.7883	0.9522	0.0044	0.1836	0.3913	0.0
0.0547	47.5	9500	0.7899	0.3997	0.4971	0.8759	nan	0.9053	0.9472	0.4999	0.8752	0.5002	0.7334	0.8557	0.2947	0.9505	0.1326	0.0	0.1843	0.0	0.8065	0.3995	0.0184	0.9146	0.2650	0.6301	0.6056	0.5749	nan	0.4294	0.6299	0.4450	0.2461	0.9515	0.8854	0.9825	0.0044	0.2045	0.5311	0.0000	0.0	0.7939	0.8655	0.4530	0.6865	0.4427	0.5983	0.6206	0.2592	0.8881	0.0597	0.0	0.1837	0.0	0.5769	0.1272	0.0183	0.8055	0.2174	0.5004	0.4960	0.3457	nan	0.3926	0.4724	0.3800	0.2072	0.8841	0.7912	0.9522	0.0037	0.1789	0.3895	0.0000
0.0543	50.0	10000	0.7826	0.3995	0.4977	0.8759	nan	0.9069	0.9471	0.5043	0.8684	0.5057	0.7351	0.8662	0.2599	0.9494	0.1607	0.0044	0.1992	0.0	0.7913	0.4628	0.0106	0.9117	0.2679	0.6351	0.5893	0.5639	nan	0.4246	0.6323	0.4266	0.2431	0.9540	0.8819	0.9827	0.0045	0.2006	0.5328	0.0	0.0	0.7947	0.8656	0.4529	0.6876	0.4461	0.5989	0.6127	0.2346	0.8877	0.0662	0.0044	0.1985	0.0	0.5765	0.1495	0.0106	0.8060	0.2190	0.5015	0.4923	0.3467	nan	0.3908	0.4693	0.3698	0.2052	0.8832	0.7906	0.9519	0.0038	0.1774	0.3885	0.0

Framework versions

Transformers 4.35.0.dev0
Pytorch 2.0.0
Datasets 2.14.5
Tokenizers 0.14.1

wang1215/segformer-b3

segformer-b3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

wang1215/
segformer-b3