Datasets:

katospiegel
/

ordfts-hackathon-pneuma-vehicles-segmentation

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train · 273 rows

Time [s] float32 80 80.4	id int32 5 379	Type stringclasses 6 values	x_img [px] int32 55 3.78k	y_img [px] int32 4 2.03k	Angle_img [rad] float32 0 6.28	Frame stringclasses 2 values	Image imagewidth (px) 80 80	Mask imagewidth (px) 80 80
80	5	Taxi	1,964	578	4.78	00001
80	21	Car	1,599	1,016	5.59	00001
80	22	Car	3,400	1,453	6.16	00001
80	25	Car	3,127	1,499	0.75	00001
80	26	Medium Vehicle	1,474	1,173	4.52	00001
80	31	Motorcycle	1,079	508	3.12	00001
80	32	Motorcycle	3,247	782	5.01	00001
80	33	Medium Vehicle	2,462	203	1.85	00001
80	34	Car	1,654	1,021	3.05	00001
80	40	Car	522	497	2.42	00001
80	65	Taxi	1,496	17	4.68	00001
80	67	Car	1,983	1,196	3.58	00001
80	87	Car	2,456	825	2.08	00001
80	94	Car	1,495	270	4.75	00001
80	95	Car	1,493	225	4.73	00001
80	103	Car	3,775	510	3.01	00001
80	104	Car	2,461	771	2.31	00001
80	105	Motorcycle	464	1,446	0.04	00001
80	106	Car	2,299	513	3.05	00001
80	108	Car	2,674	1,506	1.02	00001
80	115	Bus	2,940	1,661	4.06	00001
80	116	Motorcycle	1,449	1,250	1.64	00001
80	122	Heavy Vehicle	2,937	509	3.11	00001
80	127	Car	1,984	948	3.57	00001
80	132	Medium Vehicle	3,009	511	3.14	00001
80	139	Car	1,143	545	3.34	00001
80	148	Taxi	1,497	956	4.71	00001
80	151	Car	1,502	759	4.72	00001
80	160	Car	3,507	1,362	0.01	00001
80	171	Taxi	2,169	1,418	0.01	00001
80	176	Taxi	1,496	615	4.73	00001
80	187	Car	1,499	655	4.69	00001
80	190	Car	2,836	1,343	4.51	00001
80	192	Car	1,493	807	4.67	00001
80	194	Car	2,419	1,773	1.58	00001
80	196	Car	2,644	1,397	6.26	00001
80	199	Motorcycle	1,484	446	4.7	00001
80	203	Car	2,609	1,422	0.05	00001
80	206	Bus	1,397	1,421	0	00001
80	212	Motorcycle	1,593	1,375	6.24	00001
80	213	Car	1,519	1,372	0.01	00001
80	218	Car	2,595	1,377	6.18	00001
80	227	Motorcycle	2,734	1,370	6.11	00001
80	230	Taxi	3,474	1,434	0.04	00001
80	231	Taxi	2,697	1,406	0.03	00001
80	232	Taxi	2,320	1,373	6.28	00001
80	233	Taxi	1,928	1,417	6.28	00001
80	234	Taxi	1,499	328	4.68	00001
80	235	Taxi	1,500	413	4.74	00001
80	240	Motorcycle	1,986	356	6.16	00001
80	241	Medium Vehicle	1,302	531	3.09	00001
80	242	Motorcycle	1,506	199	4.72	00001
80	243	Motorcycle	773	516	3.15	00001
80	244	Car	1,341	1,369	0.02	00001
80	253	Motorcycle	2,440	1,803	0.62	00001
80	256	Medium Vehicle	3,014	510	3.1	00001
80	257	Motorcycle	2,515	548	2.97	00001
80	262	Motorcycle	1,702	1,394	0	00001
80	263	Motorcycle	2,183	1,401	0.07	00001
80	264	Motorcycle	1,763	1,384	6.27	00001
80	265	Car	1,838	1,396	0.01	00001
80	271	Car	306	522	3.11	00001
80	276	Motorcycle	2,756	1,394	0.06	00001
80	280	Car	3,485	1,406	0.04	00001
80	283	Car	2,745	1,419	6.25	00001
80	284	Car	2,257	1,388	0.13	00001
80	285	Car	2,311	1,396	6.26	00001
80	286	Car	1,796	1,371	0.02	00001
80	287	Motorcycle	1,697	1,426	0.03	00001
80	288	Car	1,570	1,393	6.28	00001
80	289	Car	1,118	1,399	0.01	00001
80	290	Car	3,083	1,500	3.82	00001
80	293	Car	2,860	1,416	4.33	00001
80	294	Car	2,877	1,483	4.47	00001
80	295	Car	1,495	1,463	4.69	00001
80	296	Motorcycle	2,758	1,367	6.21	00001
80	297	Car	3,605	1,528	6.24	00001
80	303	Medium Vehicle	1,500	476	4.68	00001
80	304	Taxi	1,500	172	4.73	00001
80	305	Taxi	1,556	529	3.13	00001
80	310	Taxi	1,253	1,372	6.28	00001
80	314	Car	2,404	1,930	1.63	00001
80	315	Motorcycle	1,960	527	3.17	00001
80	316	Car	1,366	534	3.06	00001
80	317	Motorcycle	2,634	1,438	6.24	00001
80	318	Car	1,431	1,394	6.26	00001
80	321	Medium Vehicle	2,429	601	1.86	00001
80	323	Car	2,421	1,632	1.56	00001
80	324	Car	2,264	534	3.13	00001
80	325	Car	2,525	1,416	0.22	00001
80	326	Car	1,114	1,295	1.58	00001
80	327	Motorcycle	2,424	1,222	1.58	00001
80	329	Motorcycle	1,479	1,383	0.02	00001
80	330	Motorcycle	1,570	1,418	6.28	00001
80	331	Car	2,410	1,272	1.57	00001
80	332	Motorcycle	1,415	1,380	0	00001
80	333	Motorcycle	3,411	519	3.06	00001
80	334	Car	1,146	1,373	6.27	00001
80	335	Car	1,058	1,394	6.27	00001
80	337	Motorcycle	2,416	1,232	1.52	00001