This model is a fine-tuned version of nvidia/segformer-b1-finetuned-ade-512-512 on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set:

Loss: 0.8640
Mean Iou: 0.1813
Mean Accuracy: 0.2233
Overall Accuracy: 0.7956
Accuracy Unlabeled: nan
Accuracy Flat-road: 0.9372
Accuracy Flat-sidewalk: 0.9555
Accuracy Flat-crosswalk: 0.0
Accuracy Flat-cyclinglane: 0.6635
Accuracy Flat-parkingdriveway: 0.0799
Accuracy Flat-railtrack: nan
Accuracy Flat-curb: 0.0001
Accuracy Human-person: 0.0
Accuracy Human-rider: 0.0
Accuracy Vehicle-car: 0.8975
Accuracy Vehicle-truck: 0.0
Accuracy Vehicle-bus: 0.0
Accuracy Vehicle-tramtrain: 0.0
Accuracy Vehicle-motorcycle: 0.0
Accuracy Vehicle-bicycle: 0.0
Accuracy Vehicle-caravan: 0.0
Accuracy Vehicle-cartrailer: 0.0
Accuracy Construction-building: 0.9214
Accuracy Construction-door: 0.0
Accuracy Construction-wall: 0.0000
Accuracy Construction-fenceguardrail: 0.0
Accuracy Construction-bridge: 0.0
Accuracy Construction-tunnel: nan
Accuracy Construction-stairs: 0.0
Accuracy Object-pole: 0.0
Accuracy Object-trafficsign: 0.0
Accuracy Object-trafficlight: 0.0
Accuracy Nature-vegetation: 0.9128
Accuracy Nature-terrain: 0.8493
Accuracy Sky: 0.9299
Accuracy Void-ground: 0.0
Accuracy Void-dynamic: 0.0
Accuracy Void-static: 0.0
Accuracy Void-unclear: 0.0
Iou Unlabeled: nan
Iou Flat-road: 0.6819
Iou Flat-sidewalk: 0.8378
Iou Flat-crosswalk: 0.0
Iou Flat-cyclinglane: 0.5970
Iou Flat-parkingdriveway: 0.0754
Iou Flat-railtrack: nan
Iou Flat-curb: 0.0001
Iou Human-person: 0.0
Iou Human-rider: 0.0
Iou Vehicle-car: 0.7287
Iou Vehicle-truck: 0.0
Iou Vehicle-bus: 0.0
Iou Vehicle-tramtrain: 0.0
Iou Vehicle-motorcycle: 0.0
Iou Vehicle-bicycle: 0.0
Iou Vehicle-caravan: 0.0
Iou Vehicle-cartrailer: 0.0
Iou Construction-building: 0.5594
Iou Construction-door: 0.0
Iou Construction-wall: 0.0000
Iou Construction-fenceguardrail: 0.0
Iou Construction-bridge: 0.0
Iou Construction-tunnel: nan
Iou Construction-stairs: 0.0
Iou Object-pole: 0.0
Iou Object-trafficsign: 0.0
Iou Object-trafficlight: 0.0
Iou Nature-vegetation: 0.7763
Iou Nature-terrain: 0.6951
Iou Sky: 0.8496
Iou Void-ground: 0.0
Iou Void-dynamic: 0.0
Iou Void-static: 0.0
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: 2
eval_batch_size: 2
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: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Unlabeled	Accuracy Flat-road	Accuracy Flat-sidewalk	Accuracy Flat-cyclinglane	Accuracy Flat-parkingdriveway	Accuracy Flat-railtrack	Accuracy Flat-curb	Accuracy Human-person	Accuracy Vehicle-car	Accuracy Vehicle-bicycle	Accuracy Construction-building	Accuracy Construction-wall	Accuracy Construction-tunnel	Accuracy Nature-vegetation	Accuracy Nature-terrain	Accuracy Sky	Accuracy Void-ground	Accuracy Void-static	Iou Unlabeled	Iou Flat-road	Iou Flat-sidewalk	Iou Flat-cyclinglane	Iou Flat-parkingdriveway	Iou Flat-railtrack	Iou Flat-curb	Iou Human-person	Iou Vehicle-car	Iou Vehicle-bicycle	Iou Construction-building	Iou Construction-wall	Iou Construction-tunnel	Iou Nature-vegetation	Iou Nature-terrain	Iou Sky	Iou Void-ground	Iou Void-static
2.9097	0.05	20	2.4129	0.0976	0.1470	0.6553	nan	0.6590	0.9442	0.0820	0.0057	nan	0.0033	0.0000	0.6208	0.0176	0.5921	0.0	nan	0.9758	0.0306	0.7320	0.0002	0.0402	0.0	0.4482	0.7368	0.0774	0.0057	0.0	0.0030	0.0000	0.4563	0.0140	0.4050	0.0	0.0	0.5114	0.0299	0.6961	0.0002	0.0302
2.2468	0.1	40	2.2262	0.1037	0.1504	0.6653	nan	0.8040	0.8979	0.0072	0.0016	nan	0.0004	0.0	0.4835	0.0006	0.8209	0.0000	nan	0.9612	0.0184	0.8068	0.0	0.0106	nan	0.4453	0.7623	0.0070	0.0016	0.0	0.0004	0.0	0.4186	0.0006	0.4512	0.0000	nan	0.5601	0.0182	0.7458	0.0	0.0104
1.925	0.15	60	2.0682	0.1164	0.1590	0.6842	nan	0.8002	0.9212	0.0815	0.0005	nan	0.0	0.0	0.4581	0.0	0.8328	0.0	nan	0.9535	0.1864	0.8544	0.0	0.0000	nan	0.4820	0.7689	0.0802	0.0005	nan	0.0	0.0	0.4091	0.0	0.4382	0.0	nan	0.5993	0.1795	0.7682	0.0	0.0000
2.3829	0.2	80	1.8790	0.1358	0.1780	0.7131	nan	0.8259	0.9218	0.2819	0.0002	nan	0.0	0.0	0.5001	0.0	0.8609	0.0	nan	0.9421	0.4796	0.8829	0.0	0.0000	nan	0.5192	0.7818	0.2719	0.0002	nan	0.0	0.0	0.4260	0.0	0.4509	0.0	nan	0.6537	0.4571	0.7836	0.0	0.0000
1.6895	0.25	100	1.7516	0.1275	0.1719	0.7009	nan	0.8793	0.8949	0.2101	0.0001	nan	0.0	0.0	0.5551	0.0	0.8658	0.0	nan	0.9496	0.2505	0.8940	0.0	0.0	nan	0.4903	0.7883	0.2059	0.0001	nan	0.0	0.0	0.4758	0.0	0.4736	0.0	nan	0.6125	0.2442	0.7891	0.0	0.0
2.0938	0.3	120	1.6307	0.1428	0.1812	0.7269	nan	0.7659	0.9636	0.2570	0.0002	nan	0.0	0.0	0.6130	0.0	0.8365	0.0	nan	0.9509	0.5413	0.8706	0.0	0.0001	nan	0.5601	0.7606	0.2490	0.0002	nan	0.0	0.0	0.5441	0.0	0.4929	0.0	nan	0.6511	0.5149	0.7966	0.0	0.0001
1.6363	0.35	140	1.4988	0.1522	0.1931	0.7447	nan	0.8234	0.9534	0.4649	0.0000	nan	0.0	0.0	0.6198	0.0	0.8628	0.0	nan	0.9422	0.6012	0.9106	0.0	0.0	nan	0.6114	0.7902	0.4050	0.0000	nan	0.0	0.0	0.5380	0.0	0.4899	0.0	nan	0.6747	0.5672	0.7937	0.0	0.0
1.7375	0.4	160	1.4685	0.1521	0.1922	0.7444	nan	0.8202	0.9583	0.4191	0.0000	nan	0.0	0.0	0.5643	0.0	0.9003	0.0	nan	0.9231	0.6577	0.9072	0.0	0.0	nan	0.6212	0.7871	0.3807	0.0000	nan	0.0	0.0	0.4978	0.0	0.4735	0.0	nan	0.7028	0.6064	0.7976	0.0	0.0
1.5994	0.45	180	1.3911	0.1559	0.1979	0.7545	nan	0.8985	0.9464	0.3306	0.0002	nan	0.0	0.0	0.7378	0.0	0.8685	0.0	nan	0.9345	0.7326	0.8831	0.0	0.0	nan	0.5703	0.8161	0.3218	0.0002	nan	0.0	0.0	0.5994	0.0	0.5232	0.0	nan	0.7016	0.6663	0.7904	0.0	0.0
2.0048	0.5	200	1.3081	0.1578	0.1978	0.7541	nan	0.7970	0.9670	0.4200	0.0003	nan	0.0	0.0	0.7116	0.0	0.8340	0.0	nan	0.9554	0.7548	0.8900	0.0	0.0	nan	0.6425	0.7818	0.3644	0.0003	nan	0.0	0.0	0.6015	0.0	0.5267	0.0	nan	0.6703	0.6692	0.7928	0.0	0.0
1.4191	0.55	220	1.2710	0.1621	0.2014	0.7633	nan	0.8450	0.9635	0.3623	0.0013	nan	0.0	0.0	0.7991	0.0	0.9186	0.0	nan	0.9269	0.7738	0.8549	0.0	0.0	nan	0.6466	0.7850	0.3533	0.0013	nan	0.0	0.0	0.6419	0.0	0.5394	0.0	nan	0.7239	0.6741	0.8204	0.0	0.0
1.2046	0.6	240	1.2596	0.1623	0.2054	0.7580	nan	0.9143	0.9102	0.6029	0.0011	nan	0.0	0.0	0.7193	0.0	0.9179	0.0	nan	0.9449	0.6649	0.8957	0.0	0.0	nan	0.5819	0.8199	0.5105	0.0011	nan	0.0	0.0	0.6122	0.0	0.5208	0.0	nan	0.6951	0.6131	0.8401	0.0	0.0
2.3716	0.65	260	1.2121	0.1652	0.2072	0.7702	nan	0.9240	0.9416	0.5131	0.0005	nan	0.0	0.0	0.8518	0.0	0.8971	0.0	nan	0.9395	0.6577	0.9059	0.0	0.0	nan	0.6001	0.8232	0.4777	0.0005	nan	0.0	0.0	0.6470	0.0	0.5700	0.0	nan	0.7144	0.6094	0.8436	0.0	0.0
1.1683	0.7	280	1.1438	0.1706	0.2117	0.7786	nan	0.8707	0.9609	0.6311	0.0007	nan	0.0	0.0	0.8191	0.0	0.9206	0.0	nan	0.9257	0.7316	0.9131	0.0	0.0	nan	0.6810	0.8121	0.5472	0.0007	nan	0.0	0.0	0.6511	0.0	0.5441	0.0	nan	0.7346	0.6461	0.8426	0.0	0.0
1.2216	0.75	300	1.1543	0.1685	0.2111	0.7765	nan	0.9293	0.9435	0.5694	0.0006	nan	0.0	0.0	0.8627	0.0	0.8790	0.0	nan	0.9470	0.7174	0.9055	0.0	0.0	nan	0.6241	0.8300	0.5381	0.0006	nan	0.0	0.0	0.6406	0.0	0.5680	0.0	nan	0.7144	0.6421	0.8326	0.0	0.0
1.0353	0.8	320	1.1280	0.1609	0.2030	0.7583	nan	0.9576	0.9160	0.4185	0.0001	nan	0.0	0.0	0.7980	0.0	0.9393	0.0	nan	0.9169	0.6555	0.8954	0.0	0.0	nan	0.5528	0.8267	0.4079	0.0001	nan	0.0	0.0	0.6672	0.0	0.5294	0.0	nan	0.7287	0.5931	0.8417	0.0	0.0
1.2914	0.85	340	1.0709	0.1669	0.2087	0.7767	nan	0.8841	0.9690	0.4128	0.0002	nan	0.0	0.0	0.8989	0.0	0.8778	0.0	nan	0.9331	0.7791	0.9226	0.0	0.0	nan	0.6624	0.8036	0.4016	0.0002	nan	0.0	0.0	0.6614	0.0	0.5705	0.0	nan	0.7446	0.6539	0.8414	0.0	0.0
1.04	0.9	360	1.0501	0.1711	0.2155	0.7789	nan	0.9411	0.9289	0.5881	0.0009	nan	0.0	0.0	0.8622	0.0	0.9081	0.0	nan	0.9310	0.8356	0.9012	0.0	0.0	nan	0.6170	0.8329	0.5329	0.0009	nan	0.0	0.0	0.6817	0.0	0.5572	0.0	nan	0.7424	0.6656	0.8460	0.0	0.0
1.3936	0.95	380	1.0156	0.1704	0.2137	0.7769	nan	0.9314	0.9340	0.5883	0.0041	nan	0.0	0.0	0.8612	0.0	0.8653	0.0	nan	0.9497	0.7963	0.9091	0.0	0.0	nan	0.6222	0.8216	0.5566	0.0041	nan	0.0	0.0	0.6814	0.0	0.5725	0.0	nan	0.7117	0.6494	0.8339	0.0	0.0
1.0755	1.0	400	1.0207	0.1699	0.2136	0.7785	nan	0.9235	0.9450	0.5502	0.0009	nan	0.0	0.0	0.9010	0.0	0.8852	0.0	nan	0.9251	0.7857	0.9188	0.0	0.0	nan	0.6156	0.8246	0.5239	0.0009	nan	0.0	0.0	0.6712	0.0	0.5672	0.0	nan	0.7448	0.6645	0.8251	0.0	0.0
0.9754	1.05	420	0.9999	0.1744	0.2183	0.7868	nan	0.9275	0.9491	0.6556	0.0032	nan	0.0	0.0	0.8851	0.0	0.8986	0.0	nan	0.9194	0.8269	0.9203	0.0	0.0	nan	0.6496	0.8305	0.5666	0.0032	nan	0.0	0.0	0.6863	0.0	0.5681	0.0	nan	0.7582	0.6836	0.8350	0.0	0.0
1.2134	1.1	440	0.9553	0.1736	0.2182	0.7841	nan	0.9338	0.9361	0.6495	0.0132	nan	0.0	0.0	0.8674	0.0	0.9159	0.0	nan	0.9247	0.8396	0.9013	0.0	0.0	nan	0.6456	0.8346	0.5679	0.0131	nan	0.0	0.0	0.6805	0.0	0.5559	0.0	nan	0.7527	0.6596	0.8452	0.0	0.0
1.0486	1.15	460	0.9654	0.1713	0.2152	0.7795	nan	0.9483	0.9307	0.5519	0.0128	nan	0.0	0.0	0.8840	0.0	0.8885	0.0	nan	0.9438	0.8156	0.9099	0.0	0.0	nan	0.6150	0.8302	0.5254	0.0127	nan	0.0	0.0	0.6839	0.0	0.5836	0.0	nan	0.7245	0.6576	0.8476	0.0	0.0
1.4786	1.2	480	0.9317	0.1756	0.2183	0.7891	nan	0.9250	0.9547	0.6636	0.0136	nan	0.0	0.0	0.8882	0.0	0.9153	0.0	nan	0.9228	0.7705	0.9308	0.0	0.0	nan	0.6901	0.8278	0.5496	0.0135	nan	0.0	0.0	0.7016	0.0	0.5615	0.0	nan	0.7577	0.6675	0.8495	0.0	0.0
0.861	1.25	500	0.9472	0.1729	0.2145	0.7849	nan	0.9310	0.9541	0.5750	0.0191	nan	0.0	0.0	0.8967	0.0	0.9062	0.0	nan	0.9373	0.7201	0.9248	0.0	0.0	nan	0.6869	0.8236	0.5153	0.0188	nan	0.0	0.0	0.6985	0.0	0.5649	0.0	nan	0.7351	0.6375	0.8513	0.0	0.0
0.8521	1.3	520	0.9387	0.1754	0.2175	0.7852	nan	0.9459	0.9409	0.5862	0.0391	nan	0.0	0.0	0.8778	0.0	0.9179	0.0	nan	0.9274	0.8059	0.9185	0.0	0.0	nan	0.6392	0.8315	0.5525	0.0383	nan	0.0	0.0	0.7149	0.0	0.5561	0.0	nan	0.7556	0.6739	0.8523	0.0	0.0
1.8729	1.35	540	0.9124	0.1767	0.2181	0.7869	nan	0.9405	0.9449	0.6155	0.0486	nan	0.0000	0.0	0.8884	0.0	0.9140	0.0	nan	0.9311	0.7752	0.9202	0.0	0.0	nan	0.6594	0.8249	0.5603	0.0471	nan	0.0000	0.0	0.7207	0.0	0.5630	0.0	nan	0.7527	0.6722	0.8533	0.0	0.0
0.9849	1.4	560	0.9113	0.1781	0.2223	0.7883	nan	0.9474	0.9323	0.7026	0.0366	nan	0.0000	0.0	0.9012	0.0	0.9031	0.0	nan	0.9198	0.8473	0.9240	0.0	0.0	nan	0.6448	0.8255	0.5907	0.0357	nan	0.0000	0.0	0.7192	0.0	0.5728	0.0	nan	0.7640	0.6903	0.8553	0.0	0.0
0.7368	1.45	580	0.9020	0.1797	0.2219	0.7919	nan	0.9410	0.9434	0.7123	0.0395	nan	0.0000	0.0	0.8789	0.0	0.9184	0.0	nan	0.9260	0.8265	0.9163	0.0	0.0	nan	0.6703	0.8327	0.6143	0.0386	nan	0.0000	0.0	0.7178	0.0	0.5594	0.0	nan	0.7605	0.7026	0.8538	0.0	0.0
0.8884	1.5	600	0.9055	0.1789	0.2201	0.7927	nan	0.9278	0.9592	0.6248	0.0399	nan	0.0	0.0	0.9106	0.0	0.9110	0.0	nan	0.9228	0.8225	0.9229	0.0	0.0	nan	0.6812	0.8265	0.5748	0.0387	nan	0.0	0.0	0.7103	0.0	0.5692	0.0	nan	0.7681	0.7012	0.8540	0.0	0.0
0.7291	1.55	620	0.9008	0.1787	0.2228	0.7900	nan	0.9461	0.9404	0.6101	0.1005	nan	0.0000	0.0	0.9062	0.0	0.9094	0.0000	nan	0.9155	0.8713	0.9290	0.0	0.0	nan	0.6276	0.8469	0.5788	0.0962	nan	0.0000	0.0	0.7110	0.0	0.5622	0.0000	nan	0.7648	0.6880	0.8430	0.0	0.0
0.7366	1.6	640	0.8804	0.1799	0.2250	0.7942	nan	0.9457	0.9424	0.7139	0.0687	nan	0.0	0.0	0.9156	0.0	0.9051	0.0	nan	0.9154	0.8601	0.9338	0.0	0.0	nan	0.6548	0.8488	0.6189	0.0666	nan	0.0	0.0	0.7035	0.0	0.5631	0.0	nan	0.7690	0.6915	0.8419	0.0	0.0
0.8606	1.65	660	0.8687	0.1812	0.2249	0.7965	nan	0.9329	0.9527	0.7145	0.0836	nan	0.0000	0.0	0.9109	0.0	0.9073	0.0000	nan	0.9181	0.8460	0.9319	0.0	0.0	nan	0.6843	0.8430	0.6109	0.0795	nan	0.0000	0.0	0.7107	0.0	0.5643	0.0000	nan	0.7685	0.6944	0.8437	0.0	0.0
0.8797	1.7	680	0.8755	0.1797	0.2211	0.7946	nan	0.9228	0.9600	0.6712	0.0447	nan	0.0000	0.0	0.8878	0.0	0.9058	0.0000	nan	0.9313	0.8406	0.9120	0.0	0.0	nan	0.7002	0.8299	0.5802	0.0434	nan	0.0000	0.0	0.7303	0.0	0.5710	0.0000	nan	0.7558	0.6902	0.8480	0.0	0.0
0.8329	1.75	700	0.8782	0.1802	0.2234	0.7910	nan	0.9567	0.9326	0.7030	0.0780	nan	0.0	0.0	0.8918	0.0	0.9066	0.0000	nan	0.9329	0.8216	0.9253	0.0	0.0	nan	0.6213	0.8476	0.6263	0.0752	nan	0.0	0.0	0.7226	0.0	0.5718	0.0000	nan	0.7541	0.6980	0.8500	0.0	0.0
0.8551	1.8	720	0.8607	0.1806	0.2231	0.7947	nan	0.9434	0.9503	0.6813	0.0720	nan	0.0000	0.0	0.8949	0.0	0.9108	0.0000	nan	0.9230	0.8410	0.9235	0.0	0.0	nan	0.6667	0.8402	0.6013	0.0691	nan	0.0000	0.0	0.7247	0.0	0.5676	0.0000	nan	0.7667	0.6951	0.8476	0.0	0.0
1.0606	1.85	740	0.8495	0.1805	0.2241	0.7945	nan	0.9486	0.9476	0.6721	0.0820	nan	0.0	0.0	0.9137	0.0	0.9072	0.0000	nan	0.9155	0.8497	0.9342	0.0	0.0	nan	0.6515	0.8446	0.6023	0.0786	nan	0.0	0.0	0.7120	0.0	0.5703	0.0000	nan	0.7725	0.6978	0.8463	0.0	0.0
0.875	1.9	760	0.8552	0.1819	0.2251	0.7966	nan	0.9384	0.9511	0.6797	0.1089	nan	0.0001	0.0	0.9089	0.0	0.9078	0.0001	nan	0.9227	0.8567	0.9292	0.0	0.0	nan	0.6766	0.8437	0.5987	0.1023	nan	0.0001	0.0	0.7202	0.0	0.5687	0.0001	nan	0.7672	0.6915	0.8501	0.0	0.0
1.1214	1.95	780	0.8541	0.1823	0.2254	0.7970	nan	0.9338	0.9521	0.6748	0.1255	nan	0.0002	0.0	0.9104	0.0	0.9056	0.0001	nan	0.9270	0.8590	0.9235	0.0	0.0	nan	0.6846	0.8435	0.5999	0.1174	nan	0.0002	0.0	0.7159	0.0	0.5677	0.0001	nan	0.7634	0.6887	0.8506	0.0	0.0
0.8599	2.0	800	0.8640	0.1813	0.2233	0.7956	nan	0.9372	0.9555	0.6635	0.0799	nan	0.0001	0.0	0.8975	0.0	0.9214	0.0000	nan	0.9128	0.8493	0.9299	0.0	0.0	nan	0.6819	0.8378	0.5970	0.0754	nan	0.0001	0.0	0.7287	0.0	0.5594	0.0000	nan	0.7763	0.6951	0.8496	0.0	0.0

Framework versions

Transformers 4.48.0
Pytorch 2.1.1+cu118
Datasets 3.2.0
Tokenizers 0.21.0

heromiya/my-fine-tuned-model

my-fine-tuned-model

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

heromiya/
my-fine-tuned-model