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
80.400002	283	Car	2,745	1,419	6.25	00002
80.400002	284	Car	2,257	1,388	0.13	00002
80.400002	285	Car	2,311	1,396	6.26	00002
80.400002	286	Car	1,820	1,371	0.04	00002
80.400002	287	Motorcycle	1,735	1,427	0.02	00002
80.400002	288	Car	1,600	1,392	6.28	00002
80.400002	289	Car	1,139	1,399	0.01	00002
80.400002	290	Car	3,081	1,500	3.82	00002
80.400002	293	Car	2,854	1,402	4.36	00002
80.400002	294	Car	2,874	1,469	4.44	00002
80.400002	295	Car	1,495	1,463	4.69	00002
80.400002	296	Motorcycle	2,758	1,367	6.21	00002
80.400002	297	Car	3,621	1,527	6.25	00002
80.400002	303	Medium Vehicle	1,501	473	4.69	00002
80.400002	304	Taxi	1,500	172	4.73	00002
80.400002	305	Taxi	1,553	529	3.15	00002
80.400002	310	Taxi	1,272	1,371	6.28	00002
80.400002	314	Car	2,403	1,950	1.59	00002
80.400002	315	Motorcycle	1,937	527	3.16	00002
80.400002	316	Car	1,356	535	3.05	00002
80.400002	317	Motorcycle	2,646	1,437	6.24	00002
80.400002	318	Car	1,456	1,394	6.27	00002
80.400002	321	Medium Vehicle	2,425	617	1.77	00002
80.400002	323	Car	2,421	1,658	1.57	00002
80.400002	324	Car	2,242	534	3.15	00002
80.400002	325	Car	2,538	1,418	0.15	00002
80.400002	326	Car	1,114	1,295	1.58	00002
80.400002	327	Motorcycle	2,424	1,222	1.58	00002
80.400002	329	Motorcycle	1,508	1,384	0.01	00002
80.400002	330	Motorcycle	1,600	1,418	6.27	00002
80.400002	331	Car	2,410	1,272	1.57	00002
80.400002	332	Motorcycle	1,442	1,381	0.02	00002
80.400002	333	Motorcycle	3,411	519	3.06	00002
80.400002	334	Car	1,163	1,372	6.27	00002
80.400002	335	Car	1,080	1,394	6.26	00002
80.400002	337	Motorcycle	2,416	1,232	1.52	00002
80.400002	339	Car	2,422	1,133	1.58	00002
80.400002	341	Car	2,414	424	1.56	00002
80.400002	342	Motorcycle	1,522	1,491	4.61	00002
80.400002	343	Car	2,725	529	3.18	00002
80.400002	345	Motorcycle	1,509	1,492	4.7	00002
80.400002	346	Car	880	1,394	0.02	00002
80.400002	348	Motorcycle	2,406	1,268	1.57	00002
80.400002	349	Car	3,107	1,428	0.01	00002
80.400002	350	Car	934	1,376	0.03	00002
80.400002	351	Car	2,658	1,419	0.01	00002
80.400002	352	Taxi	2,882	1,415	4.27	00002
80.400002	353	Car	1,503	703	4.66	00002
80.400002	354	Motorcycle	1,269	1,387	0.01	00002
80.400002	355	Motorcycle	2,867	583	3.97	00002
80.400002	356	Heavy Vehicle	3,027	542	3.13	00002
80.400002	357	Car	801	1,370	0.04	00002
80.400002	358	Taxi	2,731	1,439	6.26	00002
80.400002	359	Motorcycle	3,134	536	3.16	00002
80.400002	360	Car	693	1,367	0.01	00002
80.400002	361	Motorcycle	2,410	1,965	1.56	00002
80.400002	362	Car	2,906	1,652	4.69	00002
80.400002	363	Car	2,135	542	3.15	00002
80.400002	364	Car	545	1,366	0	00002
80.400002	365	Motorcycle	2,891	1,648	4.68	00002
80.400002	366	Motorcycle	795	1,398	0.06	00002
80.400002	367	Motorcycle	2,916	1,692	4.76	00002
80.400002	368	Car	415	1,366	0.02	00002
80.400002	369	Car	3,461	536	3.12	00002
80.400002	371	Bus	283	1,406	6.25	00002
80.400002	372	Car	3,589	544	3.15	00002
80.400002	373	Car	101	521	3.14	00002
80.400002	374	Bus	2,934	1,931	4.69	00002
80.400002	375	Motorcycle	2,462	561	2.45	00002
80.400002	376	Motorcycle	1,465	463	0.92	00002
80.400002	377	Taxi	3,711	532	3.15	00002
80.400002	378	Car	92	1,368	0.03	00002
80.400002	379	Car	2,903	2,010	4.67	00002