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-railtrack	Accuracy Flat-curb	Accuracy Human-person	Accuracy Vehicle-car	Accuracy Vehicle-bus	Accuracy Vehicle-tramtrain	Accuracy Vehicle-bicycle	Accuracy Construction-building	Accuracy Construction-wall	Accuracy Construction-fenceguardrail	Accuracy Construction-tunnel	Accuracy Object-pole	Accuracy Nature-vegetation	Accuracy Nature-terrain	Accuracy Sky	Accuracy Void-static	Iou Unlabeled	Iou Flat-road	Iou Flat-sidewalk	Iou Flat-crosswalk	Iou Flat-cyclinglane	Iou Flat-parkingdriveway	Iou Flat-railtrack	Iou Flat-curb	Iou Human-person	Iou Vehicle-car	Iou Vehicle-bus	Iou Vehicle-tramtrain	Iou Vehicle-bicycle	Iou Construction-building	Iou Construction-wall	Iou Construction-fenceguardrail	Iou Construction-tunnel	Iou Object-pole	Iou Nature-vegetation	Iou Nature-terrain	Iou Sky	Iou Void-static
1.4172	1.87	200	1.2183	0.1696	0.2214	0.7509	nan	0.8882	0.9199	0.0	0.4200	0.0164	nan	0.0	0.0	0.8778	nan	nan	0.0	0.8448	0.0	0.0	nan	0.0	0.9430	0.8044	0.9274	0.0	nan	0.5435	0.8135	0.0	0.3743	0.0160	nan	0.0	0.0	0.6044	nan	nan	0.0	0.5373	0.0	0.0	nan	0.0	0.7516	0.6550	0.7928	0.0
1.1152	3.74	400	0.8946	0.1947	0.2441	0.7852	nan	0.8535	0.9471	0.0	0.7379	0.2453	nan	0.0398	0.0	0.8882	nan	nan	0.0	0.8746	0.0061	0.0	nan	0.0014	0.9526	0.8285	0.9448	0.0019	nan	0.6355	0.8321	0.0	0.5529	0.1940	nan	0.0392	0.0	0.6807	nan	nan	0.0	0.5913	0.0061	0.0	nan	0.0014	0.7701	0.6777	0.8567	0.0019
0.6637	5.61	600	0.7447	0.2349	0.2841	0.8104	nan	0.8589	0.9451	0.4455	0.8008	0.3753	nan	0.3267	0.0380	0.8920	nan	nan	0.0	0.9227	0.0938	0.0	nan	0.0167	0.9291	0.8677	0.9557	0.0562	nan	0.6768	0.8543	0.4064	0.6414	0.2914	nan	0.2749	0.0376	0.7268	nan	nan	0.0	0.6078	0.0879	0.0	nan	0.0164	0.8005	0.6817	0.8918	0.0525
0.673	7.48	800	0.6631	0.2691	0.3202	0.8278	nan	0.8387	0.9575	0.6176	0.7938	0.4208	nan	0.3575	0.3977	0.9264	nan	nan	0.0	0.9068	0.4035	0.0	nan	0.1137	0.9495	0.8165	0.9453	0.1599	nan	0.7042	0.8567	0.5239	0.6600	0.3246	nan	0.3003	0.3212	0.7246	nan	nan	0.0	0.6749	0.3113	0.0	nan	0.1038	0.8147	0.7070	0.9008	0.1445
0.502	9.35	1000	0.6249	0.2818	0.3371	0.8345	nan	0.8332	0.9538	0.7158	0.8344	0.4079	nan	0.4420	0.4941	0.9275	nan	nan	0.0172	0.9102	0.4787	0.0253	nan	0.1454	0.9460	0.8350	0.9588	0.1887	nan	0.7176	0.8635	0.6035	0.6519	0.3246	nan	0.3545	0.3720	0.7524	nan	nan	0.0172	0.6861	0.3286	0.0250	nan	0.1309	0.8335	0.7300	0.9037	0.1584
0.9687	11.21	1200	0.5786	0.3093	0.3675	0.8471	nan	0.8703	0.9504	0.7382	0.7705	0.5297	nan	0.4804	0.6250	0.9168	nan	nan	0.1397	0.9228	0.5710	0.3183	nan	0.2252	0.9314	0.8840	0.9536	0.1981	nan	0.7380	0.8743	0.5825	0.7093	0.3829	nan	0.3743	0.4600	0.7727	nan	nan	0.1372	0.7008	0.4315	0.2847	nan	0.1930	0.8397	0.7121	0.9109	0.1761
0.4681	13.08	1400	0.5759	0.3106	0.3665	0.8462	nan	0.8586	0.9572	0.5158	0.8121	0.5195	nan	0.4539	0.6944	0.9308	nan	nan	0.2759	0.9126	0.4927	0.3145	nan	0.2566	0.9396	0.8736	0.9644	0.2226	nan	0.7134	0.8742	0.5009	0.7146	0.4018	nan	0.3726	0.4661	0.7674	nan	nan	0.2501	0.6997	0.3933	0.2827	nan	0.2137	0.8377	0.7212	0.9109	0.1964
0.5374	14.95	1600	0.5534	0.3232	0.3823	0.8518	nan	0.8607	0.9545	0.7138	0.8398	0.5129	nan	0.4823	0.7055	0.9225	nan	nan	0.3058	0.8999	0.5436	0.3798	nan	0.2878	0.9485	0.8388	0.9598	0.3145	nan	0.7336	0.8788	0.6094	0.7062	0.3966	nan	0.3854	0.4897	0.7823	nan	nan	0.2782	0.7148	0.4182	0.3304	nan	0.2324	0.8415	0.7356	0.9130	0.2491
0.6115	16.82	1800	0.5528	0.3266	0.3849	0.8539	nan	0.8521	0.9611	0.6840	0.8291	0.5057	nan	0.5070	0.7165	0.9267	nan	nan	0.3659	0.9007	0.5844	0.3961	nan	0.2827	0.9517	0.8371	0.9602	0.2848	nan	0.7414	0.8721	0.6312	0.7245	0.3979	nan	0.3987	0.4932	0.7799	nan	nan	0.2788	0.7242	0.4542	0.3464	nan	0.2326	0.8384	0.7318	0.9141	0.2386
0.4766	18.69	2000	0.5449	0.3292	0.3907	0.8555	nan	0.8585	0.9611	0.7673	0.8223	0.5127	nan	0.4937	0.7164	0.9332	nan	nan	0.3858	0.9040	0.5848	0.4417	nan	0.3156	0.9413	0.8456	0.9600	0.2780	nan	0.7447	0.8755	0.6244	0.7325	0.3997	nan	0.3974	0.4985	0.7798	nan	nan	0.2904	0.7233	0.4555	0.3734	nan	0.2484	0.8451	0.7346	0.9161	0.2359

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3

iammartian0/RoadSense_High_Definition_Street_Segmentation

segformer-b0-finetuned-segments-sidewalk

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

iammartian0/
RoadSense_High_Definition_Street_Segmentation