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diff --git a/data/Metropolis2DPointing/Metropolis2DPointing.tsv b/data/Metropolis2DPointing/Metropolis2DPointing.tsv
new file mode 100644
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+index	question_id	image_path	question	A	B	C	D
+12	000020__pair_size_4	images_annotated/000020_000020__pair_size_4.jpg	Which person is the larger of the two walking together?	1505,556	1831,721	799,777	1396,795
+15	000025__pair_size_1	images_annotated/000025_000025__pair_size_1.jpg	Which person is the larger of the two standing on the sidewalk?	1504,556	1404,796	799,777	762,771
+17	000030__quadrant_hint_0	images_annotated/000030_000030__quadrant_hint_0.jpg	Point to the car on the far right side of the road.	799,777	1832,721	762,772	741,776
+38	000065__pair_size_4	images_annotated/000065_000065__pair_size_4.jpg	Which person is the larger one in the pair?	1504,560	799,777	1446,798	762,771
+41	000070__relation_nearest_to_unique_3	images_annotated/000070_000070__relation_nearest_to_unique_3.jpg	Which car is nearest to the road sign?	799,777	763,771	742,775	1832,721
+46	000080__relation_nearest_to_unique_4	images_annotated/000080_000080__relation_nearest_to_unique_4.jpg	Which car is nearest to the road sign?	1832,721	799,777	762,771	743,776
+48	000080__pair_size_3	images_annotated/000080_000080__pair_size_3.jpg	Where is the larger person in the pair?	1504,559	1463,800	1831,721	799,777
+60	000105__quadrant_hint_8	images_annotated/000105_000105__quadrant_hint_8.jpg	Point to the car on the far right side of the image.	798,777	761,771	1832,721	740,776
+62	000110__relation_left_of_unique_2	images_annotated/000110_000110__relation_left_of_unique_2.jpg	Where is the person standing to the left of the other person?	1506,804	1504,557	1831,721	1494,804
+65	000115__largest_in_class_4	images_annotated/000115_000115__largest_in_class_4.jpg	Which person is the largest in the image?	1505,559	1831,721	799,777	1501,804
+71	000125__pair_size_0	images_annotated/000125_000125__pair_size_0.jpg	Which person is the smaller one standing on the sidewalk?	1513,805	1505,557	1526,806	799,776
+81	000150__largest_in_class_1	images_annotated/000150_000150__largest_in_class_1.jpg	Which car is the largest in the group?	733,776	760,772	797,778	685,784
+84	000155__smallest_in_class_1	images_annotated/000155_000155__smallest_in_class_1.jpg	Which car is the smallest in the image?	730,778	760,773	797,780	681,786
+90	000165__rightmost_in_class_0	images_annotated/000165_000165__rightmost_in_class_0.jpg	Point to the rightmost car in the scene.	794,781	759,774	729,780	1832,723
+95	000175__quadrant_hint_0	images_annotated/000175_000175__quadrant_hint_0.jpg	Point to the car in the lower right corner of the image.	794,786	756,778	1834,725	728,784
+97	000180__rightmost_in_class_0	images_annotated/000180_000180__rightmost_in_class_0.jpg	Find the rightmost car in the scene.	792,788	754,777	726,785	1836,728
+112	000205__quadrant_hint_1	images_annotated/000205_000205__quadrant_hint_1.jpg	Point to the road sign in the upper right section.	1818,739	1714,1079	784,794	1617,534
+116	000215__pair_size_7	images_annotated/000215_000215__pair_size_7.jpg	Point to the larger of the two road signs.	1089,712	1690,512	1699,1079	834,776
+117	000215__largest_in_class_0	images_annotated/000215_000215__largest_in_class_0.jpg	Find the largest car in the image.	1699,1079	834,776	815,785	780,795
+130	000245__largest_in_class_4	images_annotated/000245_000245__largest_in_class_4.jpg	Point to the largest car in the image.	1684,1079	825,777	791,785	748,802
+148	000275__leftmost_in_class_1	images_annotated/000275_000275__leftmost_in_class_1.jpg	Point to the leftmost car in the line of traffic.	624,849	748,798	803,781	819,772
+149	000275__relation_nearest_to_unique_4	images_annotated/000275_000275__relation_nearest_to_unique_4.jpg	Find the car nearest to the road sign.	803,781	820,772	748,798	624,849
+152	000280__leftmost_in_class_2	images_annotated/000280_000280__leftmost_in_class_2.jpg	Find the leftmost car in the line of traffic.	733,803	570,870	794,784	1693,1079
+154	000285__largest_in_class_4	images_annotated/000285_000285__largest_in_class_4.jpg	Which car is the largest in the scene?	811,777	788,789	1695,1079	709,812
+155	000285__leftmost_in_class_1	images_annotated/000285_000285__leftmost_in_class_1.jpg	Point to the black car on the left side of the road.	484,908	709,812	788,789	811,777
+159	000290__smallest_in_class_1	images_annotated/000290_000290__smallest_in_class_1.jpg	Find the smallest car.	778,796	806,783	684,824	336,982
+160	000295__rightmost_in_class_1	images_annotated/000295_000295__rightmost_in_class_1.jpg	Which car is the rightmost one in the scene?	801,783	769,799	658,833	1705,1079
+163	000300__largest_in_class_2	images_annotated/000300_000300__largest_in_class_2.jpg	Point to the largest car in the scene.	800,782	1711,1079	755,800	619,841
+173	000315__largest_in_class_8	images_annotated/000315_000315__largest_in_class_8.jpg	Tap the largest car in the image.	1810,713	1732,1079	835,762	784,786
+183	000330__rightmost_in_class_2	images_annotated/000330_000330__rightmost_in_class_2.jpg	Find the rightmost road sign.	1403,615	1736,525	46,485	1836,702
+184	000335__leftmost_in_class_1	images_annotated/000335_000335__leftmost_in_class_1.jpg	Point to the leftmost car in the line of traffic.	344,851	524,908	735,810	800,778
+186	000335__pair_size_2	images_annotated/000335_000335__pair_size_2.jpg	Find the larger of the two road signs.	15,466	1845,704	1452,605	1768,1075
+187	000340__bottommost_in_class_1	images_annotated/000340_000340__bottommost_in_class_1.jpg	Which car is at the bottom of the image?	388,978	220,860	616,799	718,819
+194	000350__smallest_in_class_1	images_annotated/000350_000350__smallest_in_class_1.jpg	Which car is the smallest in the scene?	780,779	820,764	669,837	534,815
+203	000365__quadrant_hint_8	images_annotated/000365_000365__quadrant_hint_8.jpg	Point to the car on the far right edge of the image.	816,761	802,766	1907,680	763,780
+216	000395__leftmost_in_class_4	images_annotated/000395_000395__leftmost_in_class_4.jpg	Where is the leftmost car in the traffic line?	415,838	538,797	617,797	278,835
+223	000405__leftmost_in_class_3	images_annotated/000405_000405__leftmost_in_class_3.jpg	Point to the leftmost car in the image.	250,874	446,778	163,858	495,806
+227	000415__closest_bottom_edge_4	images_annotated/000415_000415__closest_bottom_edge_4.jpg	Which car is closest to the bottom edge of the frame?	118,852	412,777	88,907	514,885
+233	000425__smallest_in_class_0	images_annotated/000425_000425__smallest_in_class_0.jpg	Point to the smallest car in the scene.	787,780	710,813	607,805	501,832
+239	000440__closest_right_edge_4	images_annotated/000440_000440__closest_right_edge_4.jpg	Find the car closest to the right edge of the image.	770,796	723,779	621,858	522,826
+240	000440__leftmost_in_class_0	images_annotated/000440_000440__leftmost_in_class_0.jpg	Point to the leftmost car on the road.	475,820	307,882	522,826	621,858
+241	000440__smallest_in_class_5	images_annotated/000440_000440__smallest_in_class_5.jpg	Where is the smallest car in the image?	770,796	621,858	724,779	522,826
+249	000455__leftmost_in_class_0	images_annotated/000455_000455__leftmost_in_class_0.jpg	Point to the leftmost car in the image.	569,801	650,803	746,808	288,994
+261	000480__bottommost_in_class_6	images_annotated/000480_000480__bottommost_in_class_6.jpg	Where is the bottommost car in the scene?	102,774	77,906	206,853	335,831
+270	000495__relation_right_of_unique_3	images_annotated/000495_000495__relation_right_of_unique_3.jpg	Find the car to the right of the rider.	776,763	1891,679	691,820	416,949
+271	000495__relation_nearest_to_unique_0	images_annotated/000495_000495__relation_nearest_to_unique_0.jpg	Point to the car closest to the rider.	691,820	416,949	776,762	160,854
+274	000500__relation_nearest_to_unique_3	images_annotated/000500_000500__relation_nearest_to_unique_3.jpg	Which car is nearest to the rider?	739,774	656,837	291,1017	772,765
+293	000535__rightmost_in_class_1	images_annotated/000535_000535__rightmost_in_class_1.jpg	Where is the rightmost cyclist on the road?	1396,791	1466,722	1469,783	1389,731
+307	000560__closest_to_center_0	images_annotated/000560_000560__closest_to_center_0.jpg	Point to the person standing closest to the center of the image.	282,802	305,785	227,783	444,839
+312	000570__leftmost_in_class_1	images_annotated/000570_000570__leftmost_in_class_1.jpg	Which car is the leftmost?	473,808	543,819	309,866	664,789
+326	000595__rightmost_in_class_0	images_annotated/000595_000595__rightmost_in_class_0.jpg	Find the car that is furthest to the right on the road.	720,771	682,776	1878,694	653,782
+345	000640__leftmost_in_class_2	images_annotated/000640_000640__leftmost_in_class_2.jpg	Point to the leftmost car parked on the street.	471,808	109,854	554,793	618,785
+346	000640__smallest_in_class_5	images_annotated/000640_000640__smallest_in_class_5.jpg	Find the smallest car among the group on the right side of the road.	710,769	685,772	660,778	618,785
+348	000645__smallest_in_class_6	images_annotated/000645_000645__smallest_in_class_6.jpg	Find the smallest car parked on the right side of the road.	677,775	650,780	604,788	705,771
+361	000670__leftmost_in_class_7	images_annotated/000670_000670__leftmost_in_class_7.jpg	Point to the leftmost car parked on the street.	384,826	205,847	441,774	516,804
+364	000675__largest_in_class_4	images_annotated/000675_000675__largest_in_class_4.jpg	Which car on the left is the largest in the image?	337,834	134,859	420,775	493,811
+370	000685__largest_in_class_3	images_annotated/000685_000685__largest_in_class_3.jpg	Find the largest car parked on the left side of the road.	54,870	375,772	217,852	437,821
+373	000690__largest_in_class_0	images_annotated/000690_000690__largest_in_class_0.jpg	Which car on the left is the largest?	348,766	404,824	503,807	140,862
+376	000695__smallest_in_class_3	images_annotated/000695_000695__smallest_in_class_3.jpg	Find the smallest car in the image.	1655,698	639,767	613,788	556,796
+395	000725__leftmost_in_class_3	images_annotated/000725_000725__leftmost_in_class_3.jpg	Point to the leftmost car parked on the street.	220,838	337,776	63,849	407,812
+398	000730__relation_nearest_to_unique_8	images_annotated/000730_000730__relation_nearest_to_unique_8.jpg	Point to the car that is nearest to the red stop sign.	786,761	594,767	535,766	814,765
+400	000735__leftmost_in_class_9	images_annotated/000735_000735__leftmost_in_class_9.jpg	Where is the leftmost car parked on the side of the road?	267,779	336,825	106,858	444,770
+418	000765__largest_in_class_9	images_annotated/000765_000765__largest_in_class_9.jpg	Point to the largest car on the left side of the road.	69,840	156,776	191,768	292,818
+419	000765__rightmost_in_class_4	images_annotated/000765_000765__rightmost_in_class_4.jpg	Point to the car on the far right of the road.	1735,711	820,757	784,773	1809,703
+422	000770__relation_nearest_to_unique_9	images_annotated/000770_000770__relation_nearest_to_unique_9.jpg	Point to the car nearest to the stop sign.	777,779	820,760	721,763	540,766
+426	000775__leftmost_in_class_0	images_annotated/000775_000775__leftmost_in_class_0.jpg	Point to the leftmost car on the road.	141,769	207,830	275,765	69,779
+430	000785__bottommost_in_class_6	images_annotated/000785_000785__bottommost_in_class_6.jpg	Where is the bottommost car in the image?	92,747	37,771	115,839	239,766
+438	000795__unique_class_6	images_annotated/000795_000795__unique_class_6.jpg	Point to the stop sign.	1804,717	1348,569	1872,711	816,761
+443	000805__largest_in_class_3	images_annotated/000805_000805__largest_in_class_3.jpg	Find the largest car on the road.	662,827	736,767	765,760	817,761
+450	000815__closest_to_center_11	images_annotated/000815_000815__closest_to_center_11.jpg	Which car is closest to the center of the image?	762,760	713,768	551,875	812,761
+478	000865__bottommost_in_class_9	images_annotated/000865_000865__bottommost_in_class_9.jpg	Point to the car that is lowest in the image.	684,759	705,756	428,760	570,789
+492	000885__quadrant_hint_10	images_annotated/000885_000885__quadrant_hint_10.jpg	Point to the car in the lower right quadrant.	1859,719	791,766	742,763	705,759
+508	000920__quadrant_hint_9	images_annotated/000920_000920__quadrant_hint_9.jpg	Find the car on the far right side of the road.	1872,720	779,775	738,767	704,763
+509	000920__leftmost_in_class_1	images_annotated/000920_000920__leftmost_in_class_1.jpg	Point to the leftmost car in the row of parked vehicles.	196,760	117,768	225,743	262,760
+510	000925__largest_in_class_0	images_annotated/000925_000925__largest_in_class_0.jpg	Which car is the largest in the image?	196,759	224,743	248,749	100,768
+514	000935__largest_in_class_2	images_annotated/000935_000935__largest_in_class_2.jpg	Which car on the left is the largest?	144,763	65,770	196,762	226,745
+515	000935__rightmost_in_class_8	images_annotated/000935_000935__rightmost_in_class_8.jpg	Find the rightmost car in the image.	1874,720	771,779	734,770	704,765
+521	000945__leftmost_in_class_8	images_annotated/000945_000945__leftmost_in_class_8.jpg	Tap the car that is closest to the left edge of the image.	135,764	197,762	225,750	44,771
+524	000950__closest_to_center_12	images_annotated/000950_000950__closest_to_center_12.jpg	Point to the car nearest to the center of the image.	795,762	759,783	725,772	704,766
+526	000955__rightmost_in_class_1	images_annotated/000955_000955__rightmost_in_class_1.jpg	Find the car that is farthest to the right in the image.	791,763	1874,720	755,785	722,772
+528	000960__largest_in_class_0	images_annotated/000960_000960__largest_in_class_0.jpg	Find the largest car in the scene.	328,766	286,769	409,773	248,754
+530	000960__closest_to_center_3	images_annotated/000960_000960__closest_to_center_3.jpg	Where is the car closest to the center of the image?	749,787	789,762	703,766	683,771
+531	000965__rightmost_in_class_6	images_annotated/000965_000965__rightmost_in_class_6.jpg	Tap the rightmost car in the scene.	789,763	744,788	1876,720	702,761
+540	000980__leftmost_in_class_3	images_annotated/000980_000980__leftmost_in_class_3.jpg	Point to the leftmost car in the row of parked cars on the left.	115,765	187,765	220,748	244,756
+547	000990__bottommost_in_class_5	images_annotated/000990_000990__bottommost_in_class_5.jpg	Where is the car that is lowest in the frame?	699,804	758,769	783,764	401,773
+564	001020__bottommost_in_class_11	images_annotated/001020_001020__bottommost_in_class_11.jpg	Point to the car that is lowest in the frame.	557,850	698,775	385,773	748,768
+567	001025__largest_in_class_7	images_annotated/001025_001025__largest_in_class_7.jpg	Where is the largest car in the scene?	499,870	379,771	693,776	279,776
+572	001030__leftmost_in_class_10	images_annotated/001030_001030__leftmost_in_class_10.jpg	Point to the car on the far left of the road.	63,767	145,767	187,754	215,756
+583	001050__leftmost_in_class_7	images_annotated/001050_001050__leftmost_in_class_7.jpg	Where is the leftmost car on the road?	107,776	147,758	40,777	163,761
+599	001080__closest_right_edge_0	images_annotated/001080_001080__closest_right_edge_0.jpg	Find the car closest to the right edge of the frame.	760,770	721,777	680,771	801,759
+602	001085__largest_in_class_6	images_annotated/001085_001085__largest_in_class_6.jpg	Which car is the largest in the image?	645,777	677,770	718,776	503,813
+610	001100__quadrant_hint_2	images_annotated/001100_001100__quadrant_hint_2.jpg	Where is the road sign located in the lower right quadrant?	1071,698	1158,760	792,761	750,772
+628	001135__quadrant_hint_0	images_annotated/001135_001135__quadrant_hint_0.jpg	Where is the road sign in the lower right quadrant?	1207,770	766,772	718,785	1108,696
+644	001170__smallest_in_class_2	images_annotated/001170_001170__smallest_in_class_2.jpg	Which car is the smallest in the image?	728,773	669,788	616,781	1697,706
+649	001180__unique_class_1	images_annotated/001180_001180__unique_class_1.jpg	Where is the road sign located?	1366,767	1713,701	716,773	1219,656
+651	001185__rightmost_in_class_3	images_annotated/001185_001185__rightmost_in_class_3.jpg	Point to the rightmost car in the scene.	1724,701	769,762	710,774	641,794
+652	001185__smallest_in_class_4	images_annotated/001185_001185__smallest_in_class_4.jpg	Find the smallest car on the road.	710,774	641,794	588,784	770,762
+666	001215__relation_nearest_to_unique_0	images_annotated/001215_001215__relation_nearest_to_unique_0.jpg	Which sign is closest to the person on the sidewalk?	1500,583	1488,511	1706,775	1788,699
+667	001220__rightmost_in_class_2	images_annotated/001220_001220__rightmost_in_class_2.jpg	Which car is the rightmost one in the scene?	741,767	1807,696	715,763	644,786
+673	001230__largest_in_class_4	images_annotated/001230_001230__largest_in_class_4.jpg	Find the largest car in the image.	405,842	512,791	550,784	195,830
+677	001235__largest_in_class_6	images_annotated/001235_001235__largest_in_class_6.jpg	Find the largest car on the road.	487,792	533,782	342,847	133,829
+680	001240__largest_in_class_1	images_annotated/001240_001240__largest_in_class_1.jpg	Find the largest car in the image.	261,858	85,832	454,791	573,795
+685	001250__leftmost_in_class_0	images_annotated/001250_001250__leftmost_in_class_0.jpg	Find the car that is farthest to the left.	291,809	129,896	401,794	519,804
+698	001270__closest_top_edge_3	images_annotated/001270_001270__closest_top_edge_3.jpg	Find the road sign that is higher up on the pole.	265,646	262,672	288,855	476,795
+700	001275__largest_in_class_8	images_annotated/001275_001275__largest_in_class_8.jpg	Tap the largest car on the left side of the road.	434,798	581,782	188,880	633,771
+701	001275__unique_class_1	images_annotated/001275_001275__unique_class_1.jpg	Point to the heavy truck on the far right.	722,763	668,783	633,771	1100,730
+717	001305__leftmost_in_class_0	images_annotated/001305_001305__leftmost_in_class_0.jpg	Find the leftmost car parked on the street.	482,830	205,833	602,790	703,777
+736	001335__relation_nearest_to_unique_5	images_annotated/001335_001335__relation_nearest_to_unique_5.jpg	Point to the car nearest to the large yellow house.	783,787	753,767	702,774	638,784
+739	001340__smallest_in_class_3	images_annotated/001340_001340__smallest_in_class_3.jpg	Which car is the smallest one in the group of vehicles on the left?	692,775	765,791	625,787	714,772
+745	001350__quadrant_hint_4	images_annotated/001350_001350__quadrant_hint_4.jpg	Where is the vehicle in the lower right corner?	716,811	1104,734	668,779	597,791
+765	001385__pair_size_3	images_annotated/001385_001385__pair_size_3.jpg	Point to the larger of the two heavy trucks on the right.	726,735	1114,725	699,764	672,767
+769	001395__largest_in_class_7	images_annotated/001395_001395__largest_in_class_7.jpg	Where is the largest car on the road?	331,783	597,778	447,802	632,772
+772	001400__leftmost_in_class_6	images_annotated/001400_001400__leftmost_in_class_6.jpg	Point to the leftmost car on the road.	426,804	589,780	324,785	627,774
+774	001400__pair_top_bottom_7	images_annotated/001400_001400__pair_top_bottom_7.jpg	Which road sign is the top one in the pair?	23,665	325,785	426,804	27,630
+788	001425__smallest_in_class_0	images_annotated/001425_001425__smallest_in_class_0.jpg	Which car on the far right is the smallest?	1903,683	1878,685	1835,687	691,775
+794	001435__pair_size_2	images_annotated/001435_001435__pair_size_2.jpg	Which truck is the larger of the two?	728,738	852,756	1175,727	686,775
+795	001435__largest_in_class_8	images_annotated/001435_001435__largest_in_class_8.jpg	Find the largest car on the road.	72,792	530,796	588,790	182,843
+796	001440__pair_size_5	images_annotated/001440_001440__pair_size_5.jpg	Which heavy truck is the larger of the two?	1183,727	727,739	863,756	683,777
+812	001465__unique_class_3	images_annotated/001465_001465__unique_class_3.jpg	Find the heavy truck among the cars.	653,780	575,788	1208,730	492,801
+820	001480__largest_in_class_4	images_annotated/001480_001480__largest_in_class_4.jpg	Tap the largest car in the scene.	410,801	528,788	269,815	627,778
+822	001485__pair_size_0	images_annotated/001485_001485__pair_size_0.jpg	Point to the smaller of the two heavy trucks.	685,734	1229,729	617,780	509,793
+824	001490__closest_to_center_1	images_annotated/001490_001490__closest_to_center_1.jpg	Find the car closest to the center of the image.	488,796	334,815	148,832	607,780
+841	001520__smallest_in_class_3	images_annotated/001520_001520__smallest_in_class_3.jpg	Where is the smallest car in the image?	1103,756	523,796	298,822	1384,740
+847	001530__smallest_in_class_3	images_annotated/001530_001530__smallest_in_class_3.jpg	Point to the smallest heavy truck in the image.	628,733	1301,725	482,800	182,831
+848	001530__closest_top_edge_4	images_annotated/001530_001530__closest_top_edge_4.jpg	Find the road sign that is closest to the top edge of the image.	169,668	171,640	182,831	482,800
+850	001535__closest_to_center_2	images_annotated/001535_001535__closest_to_center_2.jpg	Where is the car closest to the center of the image?	1167,756	1414,739	1455,741	456,805
+851	001535__smallest_in_class_3	images_annotated/001535_001535__smallest_in_class_3.jpg	Which heavy truck is the smallest?	1311,726	456,805	618,733	116,842
+854	001540__pair_top_bottom_3	images_annotated/001540_001540__pair_top_bottom_3.jpg	Which road sign is the top one in the pair?	27,651	33,615	80,848	428,805
+855	001545__rightmost_in_class_1	images_annotated/001545_001545__rightmost_in_class_1.jpg	Where is the rightmost car in the image?	1745,708	1481,736	1436,736	1212,752
+857	001545__quadrant_hint_0	images_annotated/001545_001545__quadrant_hint_0.jpg	Point to the heavy truck in the lower-left quadrant.	1332,723	591,733	395,812	1212,752
+862	001555__rightmost_in_class_0	images_annotated/001555_001555__rightmost_in_class_0.jpg	Which car is the rightmost one in the scene?	1509,732	1460,731	1260,746	316,826
+863	001560__rightmost_in_class_3	images_annotated/001560_001560__rightmost_in_class_3.jpg	Tap the car that is farthest to the right.	1472,728	1283,743	265,828	1524,728
+866	001565__pair_left_right_0	images_annotated/001565_001565__pair_left_right_0.jpg	Point to the larger of the two heavy trucks.	536,718	1379,708	1297,738	1484,724
+868	001565__rightmost_in_class_3	images_annotated/001565_001565__rightmost_in_class_3.jpg	Which car is the rightmost one?	1540,725	1484,724	1297,738	206,824
+887	001600__closest_to_center_1	images_annotated/001600_001600__closest_to_center_1.jpg	Find the car closest to the center of the image.	725,766	479,770	408,766	778,762
+899	001620__relation_nearest_to_unique_5	images_annotated/001620_001620__relation_nearest_to_unique_5.jpg	Point to the heavy truck nearest to the person.	176,712	1569,687	1756,707	1781,699
+900	001620__largest_in_class_1	images_annotated/001620_001620__largest_in_class_1.jpg	Which vehicle is the largest in the scene?	177,712	1569,687	292,769	363,769
+903	001625__quadrant_hint_0	images_annotated/001625_001625__quadrant_hint_0.jpg	Where is the person standing on the grass by the food truck?	1811,696	1784,703	1889,678	1689,702
+905	001630__largest_in_class_3	images_annotated/001630_001630__largest_in_class_3.jpg	Which is the largest vehicle in the image?	1618,682	265,770	339,771	63,705
+906	001635__relation_nearest_to_unique_3	images_annotated/001635_001635__relation_nearest_to_unique_3.jpg	Find the car nearest to the person on the right.	1831,701	1910,679	1753,701	767,768
+911	001640__relation_nearest_to_unique_2	images_annotated/001640_001640__relation_nearest_to_unique_2.jpg	Which person is closest to the heavy truck?	1730,714	1904,689	1787,696	1671,677
+912	001645__largest_in_class_6	images_annotated/001645_001645__largest_in_class_6.jpg	Point to the largest car on the far right of the image.	1816,697	1882,698	823,763	766,768
+916	001650__leftmost_in_class_0	images_annotated/001650_001650__leftmost_in_class_0.jpg	Point to the leftmost car on the road.	298,774	382,777	213,772	684,774
+917	001650__unique_class_8	images_annotated/001650_001650__unique_class_8.jpg	Where is the heavy truck parked on the right?	1788,713	1728,673	1844,696	1896,696
+919	001655__leftmost_in_class_6	images_annotated/001655_001655__leftmost_in_class_6.jpg	Point to the leftmost car in the image.	286,774	372,777	200,770	681,774
+931	001675__quadrant_hint_7	images_annotated/001675_001675__quadrant_hint_7.jpg	Where is the vehicle in the lower right corner?	1839,659	818,771	755,776	715,772
+959	001720__quadrant_hint_3	images_annotated/001720_001720__quadrant_hint_3.jpg	Where is the person standing on the far right?	804,774	733,779	1311,762	693,775
+972	001745__quadrant_hint_0	images_annotated/001745_001745__quadrant_hint_0.jpg	Find the person on the far right of the image.	1162,737	799,772	1323,754	725,776
+985	001765__smallest_in_class_4	images_annotated/001765_001765__smallest_in_class_4.jpg	Where is the smallest car in the image?	719,777	636,777	606,783	678,768
+989	001775__relation_nearest_to_unique_1	images_annotated/001775_001775__relation_nearest_to_unique_1.jpg	Where is the rider nearest to the cyclist?	1221,733	1223,776	792,773	716,777
+994	001785__largest_in_class_3	images_annotated/001785_001785__largest_in_class_3.jpg	Find the largest car on the left side of the road.	42,771	142,778	598,783	630,777
+996	001790__largest_in_class_2	images_annotated/001790_001790__largest_in_class_2.jpg	Find the largest car on the road.	41,771	598,784	630,776	140,778
+1000	001795__unique_class_2	images_annotated/001795_001795__unique_class_2.jpg	Point to the rider on the bicycle.	1273,783	1271,733	791,775	713,779
+1003	001800__rightmost_in_class_2	images_annotated/001800_001800__rightmost_in_class_2.jpg	Which car is positioned furthest to the right?	789,774	713,778	672,769	628,777
diff --git a/data/Metropolis2DPointing/images_annotated/000020_000020__pair_size_4.jpg b/data/Metropolis2DPointing/images_annotated/000020_000020__pair_size_4.jpg
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diff --git a/data/Metropolis2DPointing/images_annotated/000025_000025__pair_size_1.jpg b/data/Metropolis2DPointing/images_annotated/000025_000025__pair_size_1.jpg
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diff --git a/data/Metropolis2DPointing/images_annotated/000030_000030__quadrant_hint_0.jpg b/data/Metropolis2DPointing/images_annotated/000030_000030__quadrant_hint_0.jpg
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diff --git a/data/Metropolis2DPointing/images_annotated/000065_000065__pair_size_4.jpg b/data/Metropolis2DPointing/images_annotated/000065_000065__pair_size_4.jpg
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diff --git a/data/Metropolis2DPointing/images_annotated/000070_000070__relation_nearest_to_unique_3.jpg b/data/Metropolis2DPointing/images_annotated/000070_000070__relation_nearest_to_unique_3.jpg
new file mode 100644
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diff --git a/data/Metropolis2DPointing/images_annotated/000080_000080__pair_size_3.jpg b/data/Metropolis2DPointing/images_annotated/000080_000080__pair_size_3.jpg
new file mode 100644
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diff --git a/data/Metropolis2DPointing/images_annotated/000080_000080__relation_nearest_to_unique_4.jpg b/data/Metropolis2DPointing/images_annotated/000080_000080__relation_nearest_to_unique_4.jpg
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diff --git a/data/Metropolis2DPointing/images_annotated/000105_000105__quadrant_hint_8.jpg b/data/Metropolis2DPointing/images_annotated/000105_000105__quadrant_hint_8.jpg
new file mode 100644
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diff --git a/data/Metropolis2DPointing/images_annotated/001765_001765__smallest_in_class_4.jpg b/data/Metropolis2DPointing/images_annotated/001765_001765__smallest_in_class_4.jpg
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@@ -0,0 +1,3 @@
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diff --git a/data/Referring Expressions/RUN.md b/data/Referring Expressions/RUN.md
new file mode 100644
index 0000000000000000000000000000000000000000..d59b4f11d7c7990fa10a2c003f872a1871c65e44
--- /dev/null
+++ b/data/Referring Expressions/RUN.md	
@@ -0,0 +1,184 @@
+# How to run `prep_refdrone_data.py`
+
+This script prepares the **RefDrone test split** for public inference. It is
+strictly **web-only**: every annotation and image is downloaded from a public
+mirror on each run. The script never reads any pre-existing local copy of
+RefDrone or VisDrone elsewhere on disk.
+
+## Prerequisites
+
+- Python 3.8 or later
+- Internet access (for HuggingFace and the VisDrone image mirrors)
+- About **1 GB** of free disk for the temporary archive plus the extracted images
+
+## Web sources
+
+| What | Source | Size |
+| --- | --- | --- |
+| Annotation JSON (`RefDrone_test_mdetr.json`) | `https://huggingface.co/datasets/sunzc-sunny/RefDrone/resolve/main/RefDrone_test_mdetr.json` | ~3.3 MB |
+| Image archive (`VisDrone2019-DET-test-dev.zip`) — primary | `https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-dev.zip` (Ultralytics GitHub Release) | ~297 MB |
+| Image archive — fallback | Google Drive id `1PFdW_VFSCfZ_sTSZAGjQdifF_Xd5mf0V` via `gdown` (canonical link from `https://github.com/VisDrone/VisDrone-Dataset`) | ~297 MB |
+
+The image archive is tried in the order listed. The script falls through to the
+next mirror automatically if one fails (including the well-known Google Drive
+"too many users" quota error).
+
+## Step 1 — Navigate to the repo root
+
+```bash
+cd /path/to/iib
+```
+
+The script's repo root is the `iib/` directory. All commands below are run from
+there.
+
+## Step 2 — Install `gdown` (only required for the Google Drive fallback)
+
+```bash
+pip install gdown
+```
+
+If the primary HTTPS mirror works, `gdown` is not invoked. Installing it
+up-front is still recommended so the fallback works on machines where the
+primary mirror is unreachable.
+
+## Step 3 — Run the script
+
+```bash
+python3 scripts/refdrone/prep_refdrone_data.py
+```
+
+Flags:
+
+| Flag | Effect |
+| --- | --- |
+| `--force` | Re-download annotation and image archive even if outputs exist |
+| `--keep-zip` | Keep the downloaded `VisDrone2019-DET-test-dev.zip` after extraction (default: deleted) |
+
+## What the script does
+
+1. Downloads `RefDrone_test_mdetr.json` from HuggingFace (~3.3 MB).
+2. Parses the annotation and writes `refdrone_data.jsonl` (3,276 rows, one per
+   referring expression — no GT bboxes are ever written to this file).
+3. Downloads `VisDrone2019-DET-test-dev.zip` (~297 MB) from the first working
+   mirror, validates size, validates SHA-256, extracts the 1,503 required
+   images into `images/`, then deletes the zip (unless `--keep-zip`).
+4. Writes a `data_summary.json` report under `reports/` recording which
+   mirror was used.
+5. Hard-validates final counts: exactly **3,276** JSONL rows and exactly
+   **1,503** `.jpg` files. Any mismatch aborts with a non-zero exit code.
+
+## Output structure
+
+Outputs are written under the repo root at:
+
+```
+iib/LMUData/Spatial/2d_referring_expressions/refdrone/
+├── refdrone_data.jsonl                       # 3,276 rows
+├── annotations_raw/
+│   └── RefDrone_test_mdetr.json              # ~3.3 MB
+├── images/                                    # 1,503 .jpg files
+└── reports/
+    └── data_summary.json
+```
+
+## Step 4 — Verify success
+
+```bash
+ROOT=iib/LMUData/Spatial/2d_referring_expressions/refdrone
+
+# Annotation row count — must be 3276
+wc -l "$ROOT/refdrone_data.jsonl"
+
+# Image count — must be 1503
+find "$ROOT/images" -name '*.jpg' | wc -l
+
+# Confirm files_missing is 0 in the summary report
+python3 -c "import json; print(json.load(open('$ROOT/reports/data_summary.json'))['images']['files_missing'])"
+```
+
+A run is successful only when:
+
+- `refdrone_data.jsonl` has **exactly 3,276 lines**, and
+- `images/` contains **exactly 1,503 `.jpg` files**, and
+- The script ended with `Preparation complete (web-only download verified).`
+
+The script enforces these checks itself in step 5 of its own flow and exits
+non-zero on any deviation.
+
+## Re-running
+
+```
+python3 scripts/refdrone/prep_refdrone_data.py            # normal run; reuses prior outputs from this script
+python3 scripts/refdrone/prep_refdrone_data.py --force    # ignore prior outputs, re-download from scratch
+python3 scripts/refdrone/prep_refdrone_data.py --keep-zip # keep the 297 MB zip after extraction
+```
+
+A re-run after a partial failure is safe: if a previous run already extracted
+all 1,503 images into `images/`, the script logs that fact explicitly and
+skips re-downloading. This is the only on-disk reuse the script performs;
+external local copies of VisDrone or RefDrone elsewhere on the machine are
+never consulted.
+
+## Troubleshooting
+
+### Primary HTTPS mirror unreachable
+
+```
+[images] Mirror FAILED: ultralytics-github-release — <error>
+[images] Trying mirror: google-drive-original (gdrive)
+```
+
+The script automatically advances to the Google Drive fallback. No action
+needed unless both mirrors fail.
+
+### Google Drive quota error
+
+```
+[images] Mirror FAILED: google-drive-original — Google Drive refused the
+request (likely quota / 'too many users' throttling): ...
+```
+
+If this is the *only* failure (primary mirror succeeded), the run already
+finished correctly. If both mirrors failed, the script aborts with `ERROR: All
+image-zip mirrors failed.` Wait an hour and retry, or download the zip in a
+browser from
+`https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-dev.zip`
+and re-run; the script will resume.
+
+### Behind a firewall that blocks GitHub release assets
+
+Allowlist `release-assets.githubusercontent.com` (Microsoft Azure Blob backend
+for GitHub Releases). With that domain blocked, the primary mirror cannot
+download even the redirected URL.
+
+### `gdown` not installed
+
+```
+ERROR: gdown is required ... Install it with: pip install gdown
+```
+
+Run `pip install gdown`. The fallback Google Drive mirror needs it; the
+primary HTTPS mirror does not.
+
+### Checksum mismatch on the downloaded zip
+
+```
+ERROR: Checksum mismatch for VisDrone2019-DET-test-dev.zip
+  The file has been deleted.  Re-run the script to download again.
+```
+
+The download was truncated or the upstream file changed. The corrupt zip is
+already removed; just re-run the script. The script tries each mirror's own
+SHA-256, so a successful download from any mirror is byte-verified.
+
+### Final-validation failure
+
+```
+[validate] FAILED:
+  - <one or more issues>
+ERROR: Output validation failed.
+```
+
+The dataset is incomplete or corrupted. Run with `--force` to redo the
+downloads from scratch.
diff --git a/data/Referring Expressions/prep_refdrone_data.py b/data/Referring Expressions/prep_refdrone_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..26f06af100b0bcc6c28af1de0b769c604d90c9c8
--- /dev/null
+++ b/data/Referring Expressions/prep_refdrone_data.py	
@@ -0,0 +1,773 @@
+#!/usr/bin/env python3
+"""
+prep_refdrone_data.py
+
+Public inference data prep script for the RefDrone test split.
+Inference-only variant of prep_refdrone_eval.py — GT bboxes are never written.
+
+Produces:
+    <repo_root>/LMUData/Spatial/2d_referring_expressions/refdrone/
+    ├── refdrone_data.jsonl             # PUBLIC — one row per expression, NO GT bboxes
+    ├── annotations_raw/
+    │   └── RefDrone_test_mdetr.json
+    ├── images/
+    │   └── <1503 VisDrone test-dev images>
+    └── reports/
+        └── data_summary.json
+
+Filtering is identical to prep_refdrone_eval.py:
+  - 3,432 raw expressions → 3,276 kept (156 no-target removed)
+  - 1,595 raw images      → 1,503 kept (92 fully-empty images removed)
+
+Each JSONL row (one sentence = one row):
+    {
+        "image":    "<file_name>",
+        "sentence": "<referring expression>",
+        "width":    <int>,
+        "height":   <int>
+    }
+
+=== Web-only data acquisition (no local fallbacks) ===
+
+The script downloads everything from the internet on every run. It never
+reads from any pre-existing local copy of RefDrone or VisDrone.
+
+The only short-circuit is detecting outputs from a previous successful run
+of *this same script* in its own output directory; that is logged
+explicitly. Pass --force to re-download from scratch.
+
+Image-zip mirrors (tried in order):
+  1. Ultralytics GitHub release (direct HTTPS, ~297 MB)
+       https://github.com/ultralytics/assets/releases/download/v0.0.0/
+         VisDrone2019-DET-test-dev.zip
+  2. Google Drive (via gdown, fallback)
+       https://drive.google.com/uc?id=1PFdW_VFSCfZ_sTSZAGjQdifF_Xd5mf0V
+       (canonical link from https://github.com/VisDrone/VisDrone-Dataset)
+
+=== Usage ===
+
+  python scripts/refdrone/prep_refdrone_data.py [--force] [--keep-zip]
+
+Options:
+  --force     Re-download and re-convert even if outputs already exist.
+  --keep-zip  Keep the downloaded VisDrone zip after extraction.
+"""
+
+import argparse
+import hashlib
+import json
+import shutil
+import sys
+import zipfile
+from collections import defaultdict
+from datetime import datetime, timezone
+from pathlib import Path
+from urllib.request import urlopen, Request
+from urllib.error import URLError, HTTPError
+
+
+# ---------------------------------------------------------------------------
+# Constants
+# ---------------------------------------------------------------------------
+
+# This script lives at <repo_root>/scripts/refdrone/prep_refdrone_data.py
+REPO_ROOT = Path(__file__).resolve().parent.parent.parent
+
+DATASET_OUT_DIR = REPO_ROOT / "LMUData" / "Spatial" / "2d_referring_expressions" / "refdrone"
+ANN_RAW_DIR     = DATASET_OUT_DIR / "annotations_raw"
+IMAGES_DIR      = DATASET_OUT_DIR / "images"
+REPORTS_DIR     = DATASET_OUT_DIR / "reports"
+OUTPUT_JSONL    = DATASET_OUT_DIR / "refdrone_data.jsonl"
+SUMMARY_JSON    = REPORTS_DIR / "data_summary.json"
+
+ANN_FILENAME = "RefDrone_test_mdetr.json"
+ANN_RAW_PATH = ANN_RAW_DIR / ANN_FILENAME
+
+# RefDrone test annotation — direct download from HuggingFace (no auth required)
+HF_ANN_URL = (
+    "https://huggingface.co/datasets/sunzc-sunny/RefDrone"
+    "/resolve/main/RefDrone_test_mdetr.json"
+)
+
+# VisDrone 2019 DET test-dev image set — public mirrors (web-only)
+# Canonical source: https://github.com/VisDrone/VisDrone-Dataset
+VISDRONE_SOURCE_URL = "https://github.com/VisDrone/VisDrone-Dataset"
+GDRIVE_ZIP_NAME     = "VisDrone2019-DET-test-dev.zip"
+
+# Mirrors are tried in order. Each entry has its own SHA-256 because
+# different mirrors may re-pack the archive (different zip metadata) while
+# containing the same JPEG bytes.
+IMAGE_ZIP_MIRRORS = [
+    {
+        "name":   "ultralytics-github-release",
+        "kind":   "https",
+        "url":    "https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-dev.zip",
+        "sha256": "b28a94b06dfd9e36ce77ff8155fb82b9d2c030f198a76105933bd56c6ea6a68d",
+    },
+    {
+        "name":      "google-drive-original",
+        "kind":      "gdrive",
+        "url":       "https://drive.google.com/uc?id=1PFdW_VFSCfZ_sTSZAGjQdifF_Xd5mf0V",
+        "gdrive_id": "1PFdW_VFSCfZ_sTSZAGjQdifF_Xd5mf0V",
+        "sha256":    "78b0c5078a14ee43d0b803a354e76016d7260d1704cfd1c2dc821858d839e261",
+    },
+]
+
+# The image archive must be at least this many bytes; both known mirrors
+# report ~311 MB. A truncated download fails this gate before SHA check.
+IMAGE_ZIP_MIN_BYTES = 300_000_000
+
+# Expected counts — used as hard validation gates after extraction.
+EXPECTED_TOTAL_EXPRESSIONS = 3432
+EXPECTED_KEPT_EXPRESSIONS  = 3276
+EXPECTED_UNIQUE_IMAGES     = 1503
+
+# Fields that must never appear in a public inference row
+_FORBIDDEN_GT_FIELDS = frozenset({
+    "bbox", "bboxes", "gt_bboxes", "answer", "gpt", "annotations",
+})
+
+
+# ---------------------------------------------------------------------------
+# Logging
+# ---------------------------------------------------------------------------
+
+def log(msg: str = "") -> None:
+    print(msg, flush=True)
+
+
+# ---------------------------------------------------------------------------
+# gdown dependency management
+# ---------------------------------------------------------------------------
+
+def ensure_gdown() -> None:
+    """
+    Verify that gdown is importable.  Exits with an actionable message if not.
+    """
+    try:
+        import gdown  # noqa: F401
+    except ImportError:
+        sys.exit(
+            "ERROR: gdown is required to download the VisDrone image archive "
+            "from Google Drive, but it is not installed.\n"
+            "  Install it with:\n"
+            "    pip install gdown\n"
+            "  Then re-run this script."
+        )
+
+
+# ---------------------------------------------------------------------------
+# Checksum validation
+# ---------------------------------------------------------------------------
+
+def verify_sha256(path: Path, expected: str) -> None:
+    """Compute SHA-256 of *path* and exit with a clear error if it does not match."""
+    log(f"  Verifying checksum of {path.name} ...")
+    h = hashlib.sha256()
+    with open(path, "rb") as f:
+        for block in iter(lambda: f.read(1 << 20), b""):
+            h.update(block)
+    actual = h.hexdigest()
+    if actual != expected:
+        path.unlink(missing_ok=True)
+        sys.exit(
+            f"ERROR: Checksum mismatch for {path.name}\n"
+            f"  Expected : {expected}\n"
+            f"  Got      : {actual}\n"
+            "  The file has been deleted.  Re-run the script to download again."
+        )
+    log(f"  Checksum OK ({actual[:16]}...)")
+
+
+# ---------------------------------------------------------------------------
+# Generic download helper
+# ---------------------------------------------------------------------------
+
+def download_file(url: str, dest: Path, label: str = "") -> None:
+    """Stream-download *url* into *dest* with a progress indicator."""
+    label = label or dest.name
+    log(f"  Downloading {label} ...")
+    log(f"    URL: {url}")
+    req = Request(url, headers={"User-Agent": "Mozilla/5.0"})
+    try:
+        with urlopen(req, timeout=120) as resp, open(dest, "wb") as out:
+            total = int(resp.headers.get("Content-Length", 0))
+            downloaded = 0
+            chunk = 1 << 18  # 256 KB
+            while True:
+                block = resp.read(chunk)
+                if not block:
+                    break
+                out.write(block)
+                downloaded += len(block)
+                if total:
+                    pct = downloaded / total * 100
+                    print(
+                        f"\r    {downloaded/1e6:.1f} / {total/1e6:.1f} MB"
+                        f"  ({pct:.0f}%)",
+                        end="", flush=True,
+                    )
+            print()
+    except (HTTPError, URLError) as exc:
+        if dest.exists():
+            dest.unlink()
+        sys.exit(f"\nERROR: Download failed for {url}\n  Reason: {exc}")
+    log(f"  Saved {dest.stat().st_size / 1e6:.1f} MB -> {dest}")
+
+
+# ---------------------------------------------------------------------------
+# Step 1 — Annotation
+# ---------------------------------------------------------------------------
+
+def ensure_annotation(force: bool) -> tuple:
+    """
+    Download the RefDrone test annotation from HuggingFace.
+
+    Web-only: never reads from any pre-existing local copy. The only "skip"
+    case is detecting a previous successful download by *this same script*
+    (i.e. the file already lives at ANN_RAW_PATH inside the script's own
+    output tree); that case is logged explicitly. Pass --force to ignore it
+    and re-download.
+
+    Returns (path, strategy_string).
+    """
+    ANN_RAW_DIR.mkdir(parents=True, exist_ok=True)
+
+    if ANN_RAW_PATH.exists() and not force:
+        log(f"[annotation] Reusing previous download by this script "
+            f"({ANN_RAW_PATH.stat().st_size/1e6:.1f} MB at {ANN_RAW_PATH}). "
+            f"Pass --force to re-download.")
+        return ANN_RAW_PATH, "reused_previous_download"
+
+    log(f"[annotation] Downloading from HuggingFace ...")
+    download_file(HF_ANN_URL, ANN_RAW_PATH, label=ANN_FILENAME)
+    if not ANN_RAW_PATH.exists() or ANN_RAW_PATH.stat().st_size == 0:
+        sys.exit(
+            f"ERROR: Annotation download produced no file at {ANN_RAW_PATH}.\n"
+            f"  Source: {HF_ANN_URL}"
+        )
+    return ANN_RAW_PATH, f"downloaded:{HF_ANN_URL}"
+
+
+# ---------------------------------------------------------------------------
+# Step 2 — Parse and convert annotations
+# ---------------------------------------------------------------------------
+
+def load_annotation(ann_path: Path) -> dict:
+    with open(ann_path, encoding="utf-8") as f:
+        data = json.load(f)
+    required = {"images", "annotations", "categories"}
+    if not isinstance(data, dict) or not required.issubset(data.keys()):
+        sys.exit(f"ERROR: {ann_path} is not a valid MDETR-style JSON.")
+    return data
+
+
+def convert_annotations(data: dict) -> tuple:
+    """
+    Convert MDETR-style RefDrone data into flat public inference records.
+
+    Filtering is identical to prep_refdrone_eval.py:
+      - Expressions where every annotation has "empty": True are skipped.
+
+    Each output record contains exactly: image, sentence, width, height.
+    No GT bboxes are included.
+
+    Returns:
+        records        : list of flat dicts — one entry per referring expression
+        stats          : summary counters
+        required_files : set of unique image filenames needed
+    """
+    by_expr: dict = defaultdict(list)
+    for ann in data["annotations"]:
+        by_expr[ann["image_id"]].append(ann)
+
+    records:        list = []
+    required_files: set  = set()
+
+    stats = dict(
+        total_expressions  = len(data["images"]),
+        kept               = 0,
+        skipped_no_target  = 0,
+    )
+
+    for img_entry in data["images"]:
+        expr_id   = img_entry["id"]
+        file_name = img_entry["file_name"]
+        caption   = img_entry["caption"]
+        img_w     = img_entry["width"]
+        img_h     = img_entry["height"]
+
+        anns         = by_expr.get(expr_id, [])
+        is_no_target = all(a.get("empty", False) for a in anns) if anns else True
+
+        # --- identical filter from prep_refdrone_eval.py ---
+        if is_no_target:
+            stats["skipped_no_target"] += 1
+            continue
+
+        records.append({
+            "image":    file_name,
+            "sentence": caption,
+            "width":    img_w,
+            "height":   img_h,
+        })
+
+        required_files.add(file_name)
+        stats["kept"] += 1
+
+    return records, stats, required_files
+
+
+# ---------------------------------------------------------------------------
+# Step 3 — Images (web-only, multi-mirror)
+# ---------------------------------------------------------------------------
+
+def _extract_needed(zip_path: Path, required_files: set, force: bool) -> dict:
+    IMAGES_DIR.mkdir(parents=True, exist_ok=True)
+    log(f"  Extracting required images from zip ({zip_path.stat().st_size/1e6:.0f} MB) ...")
+    copied, skipped, not_in_zip = 0, 0, []
+    with zipfile.ZipFile(zip_path, "r") as zf:
+        idx = {Path(n).name: n for n in zf.namelist() if n.lower().endswith(".jpg")}
+        for fname in sorted(required_files):
+            dest = IMAGES_DIR / fname
+            if dest.exists() and not force:
+                skipped += 1
+                continue
+            entry = idx.get(fname)
+            if entry is None:
+                not_in_zip.append(fname)
+                continue
+            with zf.open(entry) as src, open(dest, "wb") as out:
+                shutil.copyfileobj(src, out)
+            copied += 1
+    return dict(copied=copied, skipped=skipped, not_in_zip=not_in_zip)
+
+
+def _validate_zip_size(zip_path: Path) -> None:
+    """Reject obviously-truncated archives before SHA-256 is even attempted."""
+    if not zip_path.exists():
+        raise RuntimeError(f"zip not present after download: {zip_path}")
+    size = zip_path.stat().st_size
+    if size < IMAGE_ZIP_MIN_BYTES:
+        raise RuntimeError(
+            f"zip is {size/1e6:.1f} MB, expected at least "
+            f"{IMAGE_ZIP_MIN_BYTES/1e6:.0f} MB — looks truncated"
+        )
+
+
+def _download_https(url: str, dest_zip: Path) -> None:
+    log(f"  Downloading VisDrone test-dev zip via HTTPS ...")
+    log(f"    URL  : {url}")
+    log(f"    Dest : {dest_zip}")
+    download_file(url, dest_zip, label=GDRIVE_ZIP_NAME)
+
+
+def _download_gdrive(gdrive_id: str, dest_zip: Path) -> None:
+    """
+    Google Drive download via gdown.
+
+    Cleanly handles the common "Too many users" quota error
+    (gdown.exceptions.FileURLRetrievalError) by re-raising as RuntimeError so
+    the mirror dispatcher can move on to the next mirror without exiting.
+    """
+    ensure_gdown()
+    import gdown
+    from gdown.exceptions import FileURLRetrievalError
+
+    log(f"  Downloading VisDrone test-dev zip from Google Drive ...")
+    log(f"    File ID : {gdrive_id}")
+    log(f"    Dest    : {dest_zip}")
+    log(f"    Size    : ~297 MB — this may take a few minutes")
+
+    try:
+        result = gdown.download(
+            id=gdrive_id,
+            output=str(dest_zip),
+            quiet=False,
+            resume=True,
+        )
+    except FileURLRetrievalError as exc:
+        raise RuntimeError(
+            f"Google Drive refused the request (likely quota / "
+            f"'too many users' throttling): {exc}"
+        ) from exc
+
+    if result is None or not dest_zip.exists():
+        raise RuntimeError(
+            "gdown returned no file. Google Drive may be throttling this "
+            "asset; the script will try the next mirror."
+        )
+
+
+def _try_mirror(mirror: dict, dest_zip: Path) -> None:
+    """Download a single mirror to dest_zip and verify its bytes."""
+    if mirror["kind"] == "https":
+        _download_https(mirror["url"], dest_zip)
+    elif mirror["kind"] == "gdrive":
+        _download_gdrive(mirror["gdrive_id"], dest_zip)
+    else:
+        raise RuntimeError(f"unknown mirror kind: {mirror['kind']!r}")
+    _validate_zip_size(dest_zip)
+    verify_sha256(dest_zip, mirror["sha256"])
+
+
+def ensure_images(required_files: set, force: bool, keep_zip: bool) -> dict:
+    """
+    Acquire the required VisDrone images. Web-only.
+
+    Behavior:
+      A. If every required image is already present in IMAGES_DIR (the
+         script's own output directory) from a previous successful run of
+         this script, that fact is logged and the step is skipped. Pass
+         --force to ignore this and re-download from scratch.
+      B. Otherwise, the VisDrone test-dev zip is downloaded from the first
+         working mirror in IMAGE_ZIP_MIRRORS (HTTPS first, Google Drive
+         second), validated by size and SHA-256, then the needed images are
+         extracted into IMAGES_DIR.
+
+    No external local copy of VisDrone or RefDrone is ever consulted. The
+    only on-disk reuse is the script's own prior output, which is logged.
+    """
+    IMAGES_DIR.mkdir(parents=True, exist_ok=True)
+
+    already_present = {f for f in required_files if (IMAGES_DIR / f).exists()}
+    if len(already_present) == len(required_files) and not force:
+        log(f"[images] All {len(required_files)} images already extracted by a "
+            f"previous run of this script into {IMAGES_DIR} — reusing. "
+            f"Pass --force to re-download.")
+        return dict(
+            strategy = "reused_previous_extraction",
+            copied   = 0,
+            skipped  = len(already_present),
+            missing  = [],
+            mirror   = None,
+        )
+
+    still_needed = required_files - already_present if not force else set(required_files)
+    log(f"[images] {len(already_present)} already extracted by prior run; "
+        f"{len(still_needed)} need to be downloaded.")
+
+    tmp_zip = DATASET_OUT_DIR / GDRIVE_ZIP_NAME
+    if tmp_zip.exists() and not force:
+        log(f"  [resume] Partial or complete zip from a prior run found at "
+            f"{tmp_zip} — will validate and reuse if intact.")
+
+    mirror_used = None
+    mirror_errors: list = []
+    for mirror in IMAGE_ZIP_MIRRORS:
+        log(f"[images] Trying mirror: {mirror['name']} ({mirror['kind']})")
+        log(f"          URL: {mirror['url']}")
+        try:
+            _try_mirror(mirror, tmp_zip)
+            mirror_used = mirror["name"]
+            log(f"[images] Mirror OK: {mirror['name']}")
+            break
+        except Exception as exc:
+            log(f"[images] Mirror FAILED: {mirror['name']} — {exc}")
+            mirror_errors.append(f"{mirror['name']}: {exc}")
+            if tmp_zip.exists():
+                try:
+                    tmp_zip.unlink()
+                except OSError:
+                    pass
+
+    if mirror_used is None:
+        sys.exit(
+            "ERROR: All image-zip mirrors failed. Tried:\n  - "
+            + "\n  - ".join(mirror_errors)
+            + "\n\n"
+              "Suggestions:\n"
+              "  * Check internet access.\n"
+              "  * If Google Drive is quota-throttled, try again in a few hours.\n"
+              "  * The Ultralytics mirror is hosted on GitHub Releases; if a\n"
+              "    corporate firewall blocks GitHub asset downloads, allowlist\n"
+              "    release-assets.githubusercontent.com.\n"
+        )
+
+    result = _extract_needed(tmp_zip, still_needed, force)
+    missing = [f for f in still_needed if not (IMAGES_DIR / f).exists()]
+    result.update(
+        strategy = f"downloaded_from:{mirror_used}",
+        mirror   = mirror_used,
+        missing  = missing,
+    )
+
+    if not keep_zip:
+        log(f"  Deleting downloaded zip to free space ({tmp_zip.stat().st_size/1e6:.0f} MB) ...")
+        tmp_zip.unlink()
+        log("  Zip deleted.")
+    else:
+        log(f"  Keeping zip at {tmp_zip} (--keep-zip set).")
+
+    return result
+
+
+# ---------------------------------------------------------------------------
+# Step 4 — Write outputs
+# ---------------------------------------------------------------------------
+
+def _gt_field_present(record: dict) -> bool:
+    """Return True if any forbidden GT bbox field is found in a record."""
+    return bool(_FORBIDDEN_GT_FIELDS & record.keys())
+
+
+def write_jsonl(records: list, force: bool) -> None:
+    """Write the public inference file.  Aborts if any GT field is detected."""
+    if OUTPUT_JSONL.exists() and not force:
+        log(f"[output] {OUTPUT_JSONL.name} already exists — skipping (use --force to overwrite).")
+        return
+
+    leaked = [r.get("image", "?") for r in records if _gt_field_present(r)]
+    if leaked:
+        sys.exit(
+            f"ERROR: GT bbox fields detected in {len(leaked)} inference record(s). "
+            "Aborting to prevent leakage.\n"
+            f"  First offending image: {leaked[0]}"
+        )
+
+    DATASET_OUT_DIR.mkdir(parents=True, exist_ok=True)
+    with open(OUTPUT_JSONL, "w", encoding="utf-8") as f:
+        for record in records:
+            f.write(json.dumps(record, ensure_ascii=False) + "\n")
+    log(
+        f"[output] Written: {OUTPUT_JSONL}  "
+        f"({OUTPUT_JSONL.stat().st_size/1e6:.2f} MB, {len(records)} records)"
+    )
+
+
+def write_summary(
+    conv_stats:     dict,
+    img_stats:      dict,
+    required_files: set,
+    ann_strategy:   str,
+) -> dict:
+    REPORTS_DIR.mkdir(parents=True, exist_ok=True)
+
+    images_on_disk = len(list(IMAGES_DIR.glob("*.jpg"))) if IMAGES_DIR.exists() else 0
+    missing        = img_stats.get("missing", [])
+
+    report = {
+        "generated_at": datetime.now(timezone.utc).isoformat(),
+        "script":       "prep_refdrone_data.py",
+        "annotation": {
+            "source_url": HF_ANN_URL,
+            "strategy":   ann_strategy,
+            "path":       str(ANN_RAW_PATH),
+        },
+        "images": {
+            "source_url":                VISDRONE_SOURCE_URL,
+            "mirrors_configured":        [m["name"] for m in IMAGE_ZIP_MIRRORS],
+            "mirror_used":               img_stats.get("mirror"),
+            "acquisition_strategy":      img_stats.get("strategy", "unknown"),
+            "unique_files_referenced":   len(required_files),
+            "unique_files_on_disk":      images_on_disk,
+            "files_copied_or_extracted": img_stats.get("copied", 0),
+            "files_already_present":     img_stats.get("skipped", 0),
+            "files_missing":             len(missing),
+            "missing_filenames":         missing[:20],
+        },
+        "expressions": {
+            "total_in_source":   conv_stats["total_expressions"],
+            "kept":              conv_stats["kept"],
+            "skipped_no_target": conv_stats["skipped_no_target"],
+        },
+        "gt_privacy": {
+            "gt_excluded_from_output_file": True,
+            "output_schema":               ["image", "sentence", "width", "height"],
+        },
+        "output_paths": {
+            "dataset_root": str(DATASET_OUT_DIR),
+            "output_jsonl": str(OUTPUT_JSONL),
+            "annotations":  str(ANN_RAW_PATH),
+            "images_dir":   str(IMAGES_DIR),
+            "report":       str(SUMMARY_JSON),
+        },
+    }
+
+    with open(SUMMARY_JSON, "w", encoding="utf-8") as f:
+        json.dump(report, f, indent=2)
+    log(f"[report] Written: {SUMMARY_JSON}")
+    return report
+
+
+# ---------------------------------------------------------------------------
+# Step 5 — Final validation (hard fail on any issue)
+# ---------------------------------------------------------------------------
+
+def validate_outputs(conv_stats: dict, required_files: set) -> None:
+    """
+    Hard-fail validation of every output produced by the script.
+
+    Checks:
+      - directory structure exists
+      - annotation file is non-empty
+      - JSONL row count matches the kept-expression count and equals
+        EXPECTED_KEPT_EXPRESSIONS (3,276)
+      - every required image is present on disk
+      - .jpg count in IMAGES_DIR equals EXPECTED_UNIQUE_IMAGES (1,503)
+    """
+    log("[validate] Verifying output artifacts ...")
+    errors: list = []
+
+    for d in (DATASET_OUT_DIR, ANN_RAW_DIR, IMAGES_DIR, REPORTS_DIR):
+        if not d.is_dir():
+            errors.append(f"directory missing: {d}")
+
+    if not ANN_RAW_PATH.exists() or ANN_RAW_PATH.stat().st_size == 0:
+        errors.append(f"annotation missing or empty: {ANN_RAW_PATH}")
+
+    if not OUTPUT_JSONL.exists() or OUTPUT_JSONL.stat().st_size == 0:
+        errors.append(f"output JSONL missing or empty: {OUTPUT_JSONL}")
+    else:
+        with open(OUTPUT_JSONL, encoding="utf-8") as f:
+            row_count = sum(1 for _ in f)
+        if row_count != conv_stats["kept"]:
+            errors.append(
+                f"JSONL row count {row_count} != kept expressions {conv_stats['kept']}"
+            )
+        if row_count != EXPECTED_KEPT_EXPRESSIONS:
+            errors.append(
+                f"JSONL row count {row_count} != expected {EXPECTED_KEPT_EXPRESSIONS}"
+            )
+
+    if conv_stats["total_expressions"] != EXPECTED_TOTAL_EXPRESSIONS:
+        errors.append(
+            f"raw expression count {conv_stats['total_expressions']} != "
+            f"expected {EXPECTED_TOTAL_EXPRESSIONS}"
+        )
+    if len(required_files) != EXPECTED_UNIQUE_IMAGES:
+        errors.append(
+            f"unique image count {len(required_files)} != "
+            f"expected {EXPECTED_UNIQUE_IMAGES}"
+        )
+
+    missing = [f for f in required_files if not (IMAGES_DIR / f).exists()]
+    if missing:
+        preview = ", ".join(missing[:5])
+        more = f" (+{len(missing)-5} more)" if len(missing) > 5 else ""
+        errors.append(
+            f"{len(missing)} required image(s) not present in {IMAGES_DIR}: "
+            f"{preview}{more}"
+        )
+
+    images_on_disk = len(list(IMAGES_DIR.glob("*.jpg")))
+    if images_on_disk != EXPECTED_UNIQUE_IMAGES:
+        errors.append(
+            f"{images_on_disk} .jpg files in {IMAGES_DIR}, "
+            f"expected exactly {EXPECTED_UNIQUE_IMAGES}"
+        )
+
+    if errors:
+        log("[validate] FAILED:")
+        for e in errors:
+            log(f"  - {e}")
+        sys.exit(
+            "ERROR: Output validation failed. The dataset is incomplete or "
+            "corrupted. Re-run with --force to redo the downloads from "
+            "scratch."
+        )
+
+    log(f"[validate] OK — {EXPECTED_KEPT_EXPRESSIONS} JSONL rows, "
+        f"{EXPECTED_UNIQUE_IMAGES} images on disk.")
+
+
+# ---------------------------------------------------------------------------
+# Main
+# ---------------------------------------------------------------------------
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description=(
+            "Prepare the RefDrone test split for public inference — "
+            "GT bboxes are never written by this script."
+        ),
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=__doc__,
+    )
+    parser.add_argument(
+        "--force",
+        action="store_true",
+        help="Re-download and re-convert even if outputs already exist.",
+    )
+    parser.add_argument(
+        "--keep-zip",
+        action="store_true",
+        dest="keep_zip",
+        help="Keep the downloaded VisDrone zip after extraction (default: delete).",
+    )
+    args = parser.parse_args()
+
+    log("=" * 66)
+    log("  RefDrone test split — public inference data preparation")
+    log("  Mode: STRICT WEB-ONLY (no external local fallbacks)")
+    log("=" * 66)
+    log(f"  Repo root      : {REPO_ROOT}")
+    log(f"  Output root    : {DATASET_OUT_DIR}")
+    log(f"  Output file    : {OUTPUT_JSONL.name}")
+    log(f"  Force mode     : {args.force}")
+    log(f"  Keep zip       : {args.keep_zip}")
+    log(f"  Annotation src : {HF_ANN_URL}")
+    log(f"  Image mirrors  :")
+    for m in IMAGE_ZIP_MIRRORS:
+        log(f"                   - [{m['kind']}] {m['name']}: {m['url']}")
+    log()
+
+    # --- 1. Annotation ---
+    log("[1/5] Ensuring annotation file ...")
+    ann_path, ann_strategy = ensure_annotation(args.force)
+    log(f"      Annotation path: {ann_path}")
+    log()
+
+    # --- 2. Convert and write JSONL ---
+    log("[2/5] Parsing annotations and writing inference file ...")
+    data = load_annotation(ann_path)
+    records, conv_stats, required_files = convert_annotations(data)
+    log(f"      Expressions total    : {conv_stats['total_expressions']}")
+    log(f"      Kept                 : {conv_stats['kept']}")
+    log(f"      Skipped (no-target)  : {conv_stats['skipped_no_target']}")
+    log(f"      Unique images needed : {len(required_files)}")
+    write_jsonl(records, args.force)
+    log()
+
+    # --- 3. Images ---
+    log("[3/5] Ensuring images ...")
+    img_stats = ensure_images(required_files, args.force, args.keep_zip)
+    missing = img_stats.get("missing", [])
+    if missing:
+        log(f"  {len(missing)} image(s) not yet resolved (will be re-checked in [5/5]):")
+        for f in missing[:5]:
+            log(f"    {f}")
+        if len(missing) > 5:
+            log(f"    ... and {len(missing) - 5} more")
+    else:
+        log(f"  OK — all {len(required_files)} required images are in {IMAGES_DIR}")
+    log()
+
+    # --- 4. Report ---
+    log("[4/5] Writing summary report ...")
+    report = write_summary(conv_stats, img_stats, required_files, ann_strategy)
+    log()
+
+    # --- 5. Hard validation ---
+    log("[5/5] Validating outputs ...")
+    validate_outputs(conv_stats, required_files)
+    log()
+
+    # --- Final banner ---
+    images_on_disk = report["images"]["unique_files_on_disk"]
+    log("=" * 66)
+    log("  Preparation complete (web-only download verified).")
+    log()
+    log(f"  JSONL rows       : {conv_stats['kept']} (one per referring expression)")
+    log(f"  Unique images    : {len(required_files)}")
+    log(f"  GT bboxes        : EXCLUDED — not present in output file")
+    log()
+    log(f"  Output file      : {OUTPUT_JSONL}")
+    log(f"  Images on disk   : {images_on_disk} files in {IMAGES_DIR}")
+    log(f"  Summary report   : {SUMMARY_JSON}")
+    log(f"  Image mirror used: {img_stats.get('mirror') or 'reused-prior-extraction'}")
+    log("=" * 66)
+
+
+if __name__ == "__main__":
+    main()
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diff --git a/data/tracking/README.md b/data/tracking/README.md
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+# prepare_sot_dataset.py
+
+Prepares an evaluator-ready **Single Object Tracking (SOT)** dataset from a
+benchmark JSON/JSONL file and source camera videos hosted on Hugging Face.
+
+---
+
+## Files
+
+| File | Description |
+|---|---|
+| `sot_benchmark.jsonl` | Benchmark file defining all evaluation sequences — scene, camera, object, init bounding box, and canonical 8-frame IDs. |
+| `prepare_sot_dataset.py` | Script that downloads source videos and extracts frames into an evaluator-ready directory. |
+
+---
+
+## Overview
+
+The script:
+1. Reads `sot_benchmark.jsonl` (or a custom benchmark file) describing SOT sequences (scene, camera, target bounding box, frame IDs).
+2. Downloads the corresponding source `.mp4` files from the `nvidia/PhysicalAI-SmartSpaces` Hugging Face dataset (or a custom repo).
+3. Extracts the requested frames with `ffmpeg`.
+4. Annotates the initialization frame with a bounding-box overlay (`f00_ann.png`) and saves a cropped target thumbnail (`crop.png`).
+5. Writes a `sequence_meta.json` per sequence.
+6. Produces a `gt_requests.json` manifest. **Ground-truth bounding boxes are never written to the output directory** — see [GT Request Submission](#gt-request-submission) below.
+
+---
+
+## Prerequisites
+
+| Requirement | Notes |
+|---|---|
+| Python 3.8+ | |
+| `ffmpeg` | Must be on `PATH` (or in a few well-known locations). |
+| `huggingface_hub` | `pip install huggingface_hub` |
+| `opencv-python` **or** `Pillow` | Used for bbox drawing and cropping. `opencv-python` is preferred. |
+| HF access token | Read access to the source video dataset. |
+
+```bash
+pip install huggingface_hub opencv-python
+```
+
+---
+
+## Hugging Face Authentication
+
+A token with **read** access to `nvidia/PhysicalAI-SmartSpaces` is required.
+Pass it in one of two ways:
+
+```bash
+# Option A – command-line flag
+--hf-token hf_xxxxxxxxxxxx
+
+# Option B – environment variable (recommended for scripts/CI)
+export HF_TOKEN=hf_xxxxxxxxxxxx
+```
+
+---
+
+## Input Formats
+
+The script accepts four flavors of JSON / JSONL input.
+
+### 1. Standard benchmark JSON/JSONL *(most common)*
+
+```json
+[
+  {
+    "seq_id": "Warehouse_016__Camera_11__5600__obj353",
+    "scene": "Warehouse_016",
+    "camera": "Camera_11",
+    "object_id": "353",
+    "object_type": "Robot",
+    "init_frame_id": 5600,
+    "init_bbox": [799.0, 601.9, 918.8, 956.5],
+    "canonical_frame_ids": [5600, 5615, 5630, 5645, 5660, 5675, 5690, 5705],
+    "clip_fps": 30.0
+  }
+]
+```
+
+- `init_bbox` — normalized coordinates in **thousandths** of image dimensions
+  (`[x1, y1, x2, y2]` where 1000 = full width/height).
+- `canonical_frame_ids` — preferred source-video frame indices to extract.
+  When provided and long enough, they take priority over the stride calculation.
+
+### 2. Benchmark JSONL with explicit GT
+
+```json
+{"seq_id": "...", "canonical_frame_ids": [...], "gt_bboxes": {"5600": [...]}}
+```
+
+### 3. Dataset JSONL (metadata + conversations)
+
+```json
+{
+  "id": "...",
+  "metadata": {"scene": "...", "camera": "...", "init_bbox": [...], ...},
+  "conversations": [{"role": "user", "value": "..."}, {"role": "assistant", "value": "{\"5600\": [...]}"}]
+}
+```
+
+### 4. Sequence-only JSON/JSONL
+
+```json
+{"id": "...", "scene": "...", "camera": "...", "source_frame_ids": [...], "init_bbox": [...]}
+```
+
+---
+
+## Output Structure
+
+```
+<output_dir>/
+  gt_requests.json            # submit this to us to receive GT annotations (see below)
+  <seq_id>/
+    frames/
+      f00.png                 # initialization frame
+      f00_ann.png             # initialization frame with target bbox drawn
+      crop.png                # cropped target region
+      f01.png
+      f02.png
+      ...
+      f{N-1}.png
+    sequence_meta.json        # per-sequence metadata
+```
+
+### `sequence_meta.json` fields
+
+| Field | Description |
+|---|---|
+| `frame_ids` | Source-video frame indices that were extracted |
+| `init_bbox` | Target bounding box (thousandths) |
+| `label` | Human-readable sequence label |
+| `scene` / `camera` | Source identifiers |
+| `object_id` / `object_type` | Target object metadata |
+| `stride` | Frame stride used during extraction |
+| `nframes` | Number of frames extracted |
+| `clip_fps` | Frame rate of the source video |
+| `gt_available` | Always `false` (GT is private) |
+
+---
+
+## Usage
+
+### Minimal
+
+```bash
+python scripts/tracking/prepare_sot_dataset.py \
+    --benchmark sot_benchmark.jsonl \
+    --output-dir ./SOT_prepared_8f
+```
+
+### Extract 32 frames per sequence
+
+```bash
+python scripts/tracking/prepare_sot_dataset.py \
+    --benchmark sot_benchmark.jsonl \
+    --output-dir ./SOT_prepared_32f \
+    --nframes 32
+```
+
+### Custom frame stride
+
+```bash
+python scripts/tracking/prepare_sot_dataset.py \
+    --benchmark sot_benchmark.jsonl \
+    --output-dir ./SOT_prepared \
+    --nframes 16 \
+    --stride 10
+```
+
+### Process only specific sequences
+
+```bash
+python scripts/tracking/prepare_sot_dataset.py \
+    --benchmark sot_benchmark.jsonl \
+    --output-dir ./SOT_prepared \
+    --sequences Warehouse_016__Camera_11__5600__obj353 Warehouse_016__Camera_05__704__obj352
+```
+
+### Use a custom Hugging Face cache directory
+
+```bash
+python scripts/tracking/prepare_sot_dataset.py \
+    --benchmark sot_benchmark.jsonl \
+    --output-dir ./SOT_prepared \
+    --hf-token hf_xxxxxxxxxxxx \
+    --hf-cache-dir /data/hf_cache
+```
+
+### Windows (PowerShell)
+
+```powershell
+python scripts/tracking/prepare_sot_dataset.py `
+    --benchmark sot_benchmark.jsonl `
+    --output-dir .\SOT_prepared_8f `
+    --hf-token hf_xxxxxxxxxxxx
+```
+
+---
+
+## Command-Line Reference
+
+| Argument | Required | Default | Description |
+|---|---|---|---|
+| `--benchmark` | Yes | — | Path to the input benchmark JSON or JSONL file. |
+| `--output-dir` | Yes | — | Directory where prepared sequences are written. Created if it does not exist. |
+| `--nframes` | No | `8` | Number of frames to extract per sequence. |
+| `--stride` | No | auto | Source-video frame stride. When omitted, auto-computed from `clip_end_frame` or taken directly from `canonical_frame_ids`. |
+| `--hf-token` | No* | `$HF_TOKEN` | Hugging Face access token. Falls back to the `HF_TOKEN` environment variable. |
+| `--hf-cache-dir` | No | HF default | Cache directory for downloaded videos. |
+| `--repo-id` | No | `nvidia/PhysicalAI-SmartSpaces` | Hugging Face dataset repository. |
+| `--repo-subdir` | No | `MTMC_Tracking_2025` | Subdirectory inside the repository. |
+| `--sequences` | No | all | Space-separated list of `seq_id` values to prepare. All others are skipped. |
+
+\* Required in practice unless `HF_TOKEN` is set in the environment.
+
+---
+
+## Resuming an Interrupted Run
+
+The script checks how many frames already exist in each sequence's `frames/`
+directory. If the count equals or exceeds `--nframes`, that sequence is skipped
+automatically. You can safely re-run the command after an interruption; only
+incomplete sequences will be processed.
+
+---
+
+## Frame Stride Logic
+
+Frames are selected using this priority order:
+
+1. **`canonical_frame_ids`** in the benchmark — used directly when the list
+   has at least `--nframes` entries and `--stride` is not explicitly set.
+2. **`--stride`** — fixed stride supplied by the user.
+3. **Auto** — computed as `min(15, (clip_end_frame - init_frame_id) / (nframes - 1))`,
+   capped at the default stride of 15.
+
+---
+
+## Train / Val Split
+
+The script infers the dataset split from the scene name:
+
+- `Warehouse_000` – `Warehouse_014` → **train**
+- `Warehouse_015` and above → **val**
+
+This determines which subdirectory (`train/` or `val/`) is used when
+constructing the download path on Hugging Face.
+
+---
+
+## GT Request Submission
+
+The `sot_benchmark.jsonl` file defines the canonical **8-frame** evaluation sequences with fixed frame IDs. If you use the default settings (`--nframes 8` without `--stride`), no submission is needed — the benchmark frames are already known.
+
+If you run a **custom variant** (e.g. `--nframes 16`, `--nframes 32`, `--nframes 64`, or a custom `--stride`), the script will produce a `gt_requests.json` file in your output directory once preparation is complete. **Submit this file back to us** so we can look up and return the ground-truth annotations for your chosen frames.
+
+Send `gt_requests.json` to: *(benchmark contact TBD)*
+
+---
+
+## Evaluator Integration
+
+After preparation, point your evaluator config at the output directory:
+
+```json
+{
+  "prepared_data_dir": "./SOT_prepared_8f"
+}
+```
+
+---
+
+## Troubleshooting
+
+| Problem | Fix |
+|---|---|
+| `ERROR: ffmpeg not found` | Install ffmpeg and add it to `PATH`, or place it in `/usr/bin/ffmpeg`. |
+| `401 Unauthorized` from Hugging Face | Check that `--hf-token` / `HF_TOKEN` is set and has read access to the repo. |
+| Download retries / timeouts | The script retries up to 4 times with back-off. Check your network connection. |
+| Bounding boxes not drawn | Install `opencv-python` or `Pillow`. Without either, the script copies the raw frame without annotation. |
+| Sequence not found in output | Verify the `seq_id` value; use `--sequences <seq_id>` to test a single entry. |
diff --git a/data/tracking/prepare_sot_dataset.py b/data/tracking/prepare_sot_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0c57848b7d8f407a1f6a321a7d23dea68e4e1b5
--- /dev/null
+++ b/data/tracking/prepare_sot_dataset.py
@@ -0,0 +1,680 @@
+"""
+IIB Single Object Tracking (SOT) dataset preparer.
+
+Given a benchmark JSON/JSONL file describing SOT sequences, this script
+downloads the source camera videos from the IIB Hugging Face dataset and
+reconstructs an evaluator-ready dataset:
+
+  <output_dir>/
+    <seq_id>/
+      frames/
+        f00.png        # first (initialization) frame
+        f00_ann.png    # first frame with target bounding box drawn
+        crop.png       # cropped target region from the first frame
+        f01.png ... f{N-1:02d}.png
+      sequence_meta.json
+
+Prerequisites:
+  - Python 3.8+
+  - ffmpeg on PATH
+  - huggingface_hub  (pip install huggingface_hub)
+  - A Hugging Face access token with read access to the source video dataset
+    (set via --hf-token or the HF_TOKEN environment variable)
+
+Supported input formats:
+  1. Benchmark JSON/JSONL (standard format)
+     [
+       {
+         "seq_id": "...",
+         "scene": "Warehouse_000",
+         "camera": "Camera_0010",
+         "init_frame_id": 2672,
+         "init_bbox": [...],
+         "canonical_frame_ids": [...],
+         "clip_start_frame": 2230,
+         "clip_end_frame": 3130
+       }
+     ]
+
+  2. Benchmark JSONL with canonical GT
+     {"seq_id": "...", "canonical_frame_ids": [...], "gt_bboxes": {"1": [...]}}
+
+  3. Dataset JSONL with metadata/conversations
+     {"id": "...", "metadata": {...}, "conversations": [...]}
+
+  4. Sequence-only JSON/JSONL
+     {"id": "...", "source_frame_ids": [...], "init_bbox": [...]}
+
+GT policy:
+  - Ground-truth bounding boxes are not written to the output directory.
+  - The script prepares frames and metadata only.
+  - A private GT request manifest (gt_requests.json) is written to the
+    output directory for benchmark-owner-side annotation lookup.
+
+Examples:
+  python scripts/tracking/prepare_sot_dataset.py ^
+      --benchmark sot_benchmark.jsonl ^
+      --output-dir .\\SOT_prepared_8f
+
+  python scripts/tracking/prepare_sot_dataset.py ^
+      --benchmark sot_benchmark.jsonl ^
+      --output-dir .\\SOT_prepared_32f ^
+      --nframes 32
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import shutil
+import subprocess
+import sys
+import time
+from typing import Dict, List, Optional
+
+
+DEFAULT_REPO_ID = "nvidia/PhysicalAI-SmartSpaces"  # source video dataset on Hugging Face
+DEFAULT_REPO_SUBDIR = "MTMC_Tracking_2025"
+DEFAULT_STRIDE = 15
+
+
+def find_ffmpeg() -> str:
+    for candidate in ["ffmpeg", "/usr/bin/ffmpeg", "/usr/local/bin/ffmpeg"]:
+        if shutil.which(candidate):
+            return candidate
+    for bindir in [
+        "/opt/conda/bin",
+        os.path.expanduser("~/miniconda3/bin"),
+        os.path.expanduser("~/anaconda3/bin"),
+        "/software/slurm/spackages/linux-rocky8-x86_64/gcc-9.5.0"
+        "/ffmpeg-6.0-p2yymc26idfdwvbsuijraseemhm5clvc/bin",
+    ]:
+        candidate = os.path.join(bindir, "ffmpeg")
+        if os.path.exists(candidate):
+            return candidate
+    sys.exit("ERROR: ffmpeg not found. Install it and add it to PATH.")
+
+
+def extract_frame(ffmpeg: str, video_path: str, timestamp_sec: float, out_path: str) -> None:
+    subprocess.run(
+        [
+            ffmpeg,
+            "-y",
+            "-i",
+            video_path,
+            "-ss",
+            f"{timestamp_sec:.6f}",
+            "-vframes",
+            "1",
+            "-q:v",
+            "2",
+            out_path,
+        ],
+        check=True,
+        stdout=subprocess.DEVNULL,
+        stderr=subprocess.DEVNULL,
+    )
+
+
+def draw_bbox_on_frame(frame_path: str, bbox_norm: List[float], out_path: str) -> None:
+    try:
+        import cv2
+
+        img = cv2.imread(frame_path)
+        if img is None:
+            shutil.copy(frame_path, out_path)
+            return
+        h, w = img.shape[:2]
+        x1 = int(bbox_norm[0] / 1000 * w)
+        y1 = int(bbox_norm[1] / 1000 * h)
+        x2 = int(bbox_norm[2] / 1000 * w)
+        y2 = int(bbox_norm[3] / 1000 * h)
+        cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 4)
+        cv2.putText(
+            img,
+            "TARGET",
+            (x1, max(y1 - 6, 12)),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.9,
+            (0, 255, 0),
+            2,
+        )
+        cv2.imwrite(out_path, img)
+        return
+    except ImportError:
+        pass
+
+    try:
+        from PIL import Image, ImageDraw
+
+        img = Image.open(frame_path)
+        w, h = img.size
+        draw = ImageDraw.Draw(img)
+        draw.rectangle(
+            [
+                bbox_norm[0] / 1000 * w,
+                bbox_norm[1] / 1000 * h,
+                bbox_norm[2] / 1000 * w,
+                bbox_norm[3] / 1000 * h,
+            ],
+            outline="green",
+            width=4,
+        )
+        img.save(out_path)
+        return
+    except ImportError:
+        pass
+
+    shutil.copy(frame_path, out_path)
+
+
+def crop_target(frame_path: str, bbox_norm: List[float], out_path: str) -> None:
+    try:
+        import cv2
+
+        img = cv2.imread(frame_path)
+        if img is None:
+            return
+        h, w = img.shape[:2]
+        x1 = max(0, int(bbox_norm[0] / 1000 * w))
+        y1 = max(0, int(bbox_norm[1] / 1000 * h))
+        x2 = min(w, int(bbox_norm[2] / 1000 * w))
+        y2 = min(h, int(bbox_norm[3] / 1000 * h))
+        cv2.imwrite(out_path, img[y1:y2, x1:x2])
+        return
+    except ImportError:
+        pass
+
+    try:
+        from PIL import Image
+
+        img = Image.open(frame_path)
+        w, h = img.size
+        x1 = max(0, int(bbox_norm[0] / 1000 * w))
+        y1 = max(0, int(bbox_norm[1] / 1000 * h))
+        x2 = min(w, int(bbox_norm[2] / 1000 * w))
+        y2 = min(h, int(bbox_norm[3] / 1000 * h))
+        img.crop((x1, y1, x2, y2)).save(out_path)
+    except ImportError:
+        pass
+
+
+def load_json_or_jsonl(path: str):
+    with open(path, encoding="utf-8") as f:
+        first_non_ws = ""
+        while True:
+            ch = f.read(1)
+            if not ch:
+                break
+            if not ch.isspace():
+                first_non_ws = ch
+                break
+
+    if first_non_ws in ("[", "{"):
+        try:
+            with open(path, encoding="utf-8") as f:
+                return json.load(f)
+        except json.JSONDecodeError:
+            pass
+
+    records = []
+    with open(path, encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            if line:
+                records.append(json.loads(line))
+    return records
+
+
+def normalize_gt_bboxes(raw_gt: Optional[dict]) -> Dict[str, List[float]]:
+    if not raw_gt:
+        return {}
+    out = {}
+    for key, value in raw_gt.items():
+        key_str = str(key)
+        if key_str.startswith("frame_"):
+            key_str = key_str.split("_")[-1]
+        out[key_str] = value
+    return out
+
+
+def sequence_label(seq: dict) -> str:
+    return seq.get(
+        "label",
+        f"{seq['scene']}/{seq['camera']}/{seq['init_frame_id']}/obj{seq['object_id']}",
+    )
+
+
+def normalize_sequence(rec: dict) -> dict:
+    if "metadata" in rec and "id" in rec:
+        meta = rec["metadata"]
+        canonical = meta.get("source_frame_ids") or meta.get("canonical_frame_ids") or []
+        gt_bboxes = {}
+        try:
+            answer = rec["conversations"][1]["value"]
+            parsed_gt = json.loads(answer)
+            gt_bboxes = normalize_gt_bboxes(parsed_gt)
+        except (KeyError, IndexError, TypeError, json.JSONDecodeError):
+            pass
+        return {
+            "seq_id": rec["id"],
+            "scene": meta["scene"],
+            "camera": meta["camera"],
+            "object_id": str(meta.get("object_id", "")),
+            "object_type": meta.get("object_type", "object"),
+            "init_frame_id": int(canonical[0]),
+            "init_bbox": meta["init_bbox"],
+            "canonical_frame_ids": canonical,
+            "clip_start_frame": meta.get("clip_start_frame"),
+            "clip_end_frame": meta.get("clip_end_frame", canonical[-1] if canonical else None),
+            "clip_fps": float(meta.get("clip_fps", 30.0)),
+            "gt_bboxes": gt_bboxes,
+        }
+
+    canonical = rec.get("canonical_frame_ids") or rec.get("source_frame_ids") or []
+    init_frame_id = rec.get("init_frame_id", canonical[0] if canonical else None)
+    clip_end_frame = rec.get("clip_end_frame", canonical[-1] if canonical else init_frame_id)
+    clip_start_frame = rec.get("clip_start_frame")
+    if clip_start_frame is None and init_frame_id is not None:
+        clip_start_frame = init_frame_id
+
+    seq_id = rec.get("seq_id") or rec.get("id")
+    if not seq_id:
+        raise ValueError("Sequence record is missing seq_id/id")
+    if init_frame_id is None:
+        raise ValueError(f"{seq_id}: missing init_frame_id and canonical/source frame ids")
+    if "scene" not in rec or "camera" not in rec or "init_bbox" not in rec:
+        raise ValueError(f"{seq_id}: missing required scene/camera/init_bbox fields")
+
+    return {
+        "seq_id": seq_id,
+        "scene": rec["scene"],
+        "camera": rec["camera"],
+        "object_id": str(rec.get("object_id", "")),
+        "object_type": rec.get("object_type", "object"),
+        "init_frame_id": int(init_frame_id),
+        "init_bbox": rec["init_bbox"],
+        "canonical_frame_ids": canonical,
+        "clip_start_frame": int(clip_start_frame) if clip_start_frame is not None else None,
+        "clip_end_frame": int(clip_end_frame) if clip_end_frame is not None else None,
+        "clip_fps": float(rec.get("clip_fps", 30.0)),
+        "gt_bboxes": normalize_gt_bboxes(rec.get("gt_bboxes")),
+        "label": rec.get("label"),
+    }
+
+
+def load_sequences(benchmark_path: str) -> List[dict]:
+    raw = load_json_or_jsonl(benchmark_path)
+    if isinstance(raw, dict):
+        if "sequences" in raw:
+            raw = raw["sequences"]
+        else:
+            raw = [raw]
+    return [normalize_sequence(rec) for rec in raw]
+
+
+def get_split(scene: str) -> str:
+    return "val" if int(scene.split("_")[-1]) >= 15 else "train"
+
+
+def download_hf_file(repo_id: str, filename: str, hf_token: Optional[str], hf_cache_dir: Optional[str]) -> str:
+    from huggingface_hub import hf_hub_download
+
+    return hf_hub_download(
+        repo_id=repo_id,
+        repo_type="dataset",
+        filename=filename,
+        cache_dir=hf_cache_dir,
+        token=hf_token,
+    )
+
+
+def download_hf_file_with_retries(
+    repo_id: str,
+    filename: str,
+    hf_token: Optional[str],
+    hf_cache_dir: Optional[str],
+    max_attempts: int = 4,
+) -> str:
+    last_exc = None
+    for attempt in range(1, max_attempts + 1):
+        try:
+            return download_hf_file(
+                repo_id=repo_id,
+                filename=filename,
+                hf_token=hf_token,
+                hf_cache_dir=hf_cache_dir,
+            )
+        except Exception as exc:
+            last_exc = exc
+            if attempt == max_attempts:
+                break
+            wait_sec = attempt * 2
+            print(f"        retry {attempt}/{max_attempts - 1} after error: {exc}")
+            time.sleep(wait_sec)
+    raise last_exc
+
+
+def choose_stride(
+    n_frames: int,
+    init_frame_id: int,
+    clip_end_frame: Optional[int],
+    user_stride: Optional[int],
+) -> int:
+    if user_stride is not None:
+        return user_stride
+    if n_frames <= 1:
+        return DEFAULT_STRIDE
+    if clip_end_frame is None:
+        return DEFAULT_STRIDE
+    frames_available = max(0, clip_end_frame - init_frame_id)
+    max_stride = frames_available // max(1, n_frames - 1)
+    return min(DEFAULT_STRIDE, max(1, max_stride))
+
+
+def existing_frame_count(frames_dir: str) -> int:
+    if not os.path.isdir(frames_dir):
+        return 0
+    return len(
+        [
+            name
+            for name in os.listdir(frames_dir)
+            if name.startswith("f") and name.endswith(".png") and "_ann" not in name
+        ]
+    )
+
+
+def write_gt_requests(
+    gt_requests_path: str,
+    benchmark_path: str,
+    repo_id: str,
+    repo_subdir: str,
+    nframes: int,
+    stride: Optional[int],
+    requests: List[dict],
+) -> None:
+    payload = {
+        "benchmark": os.path.abspath(benchmark_path),
+        "repo_id": repo_id,
+        "repo_subdir": repo_subdir,
+        "nframes": nframes,
+        "stride": stride,
+        "requests": requests,
+    }
+    with open(gt_requests_path, "w", encoding="utf-8") as f:
+        json.dump(payload, f, indent=2)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Prepare the IIB SOT dataset from a benchmark JSON/JSONL and source camera videos."
+    )
+    parser.add_argument(
+        "--benchmark",
+        required=True,
+        help="Input benchmark JSON or JSONL with sequence metadata.",
+    )
+    parser.add_argument(
+        "--output-dir",
+        required=True,
+        help="Output prepared_data_dir for the evaluator.",
+    )
+    parser.add_argument(
+        "--nframes",
+        type=int,
+        default=8,
+        help="Frames to extract per sequence. Default: 8.",
+    )
+    parser.add_argument(
+        "--stride",
+        type=int,
+        default=None,
+        help="Source-video frame stride. Default: auto.",
+    )
+    parser.add_argument(
+        "--hf-token",
+        default=None,
+        help="Hugging Face token. Falls back to HF_TOKEN env var.",
+    )
+    parser.add_argument(
+        "--hf-cache-dir",
+        default=None,
+        help="Optional cache directory for Hugging Face downloads.",
+    )
+    parser.add_argument(
+        "--repo-id",
+        default=DEFAULT_REPO_ID,
+        help=f"Hugging Face dataset repo id. Default: {DEFAULT_REPO_ID}.",
+    )
+    parser.add_argument(
+        "--repo-subdir",
+        default=DEFAULT_REPO_SUBDIR,
+        help=f"Dataset subdirectory inside the repo. Default: {DEFAULT_REPO_SUBDIR}.",
+    )
+    parser.add_argument(
+        "--sequences",
+        nargs="*",
+        default=None,
+        help="Optional subset of seq_ids to prepare.",
+    )
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    hf_token = args.hf_token or os.environ.get("HF_TOKEN")
+    ffmpeg = find_ffmpeg()
+
+    if not os.path.exists(args.benchmark):
+        sys.exit(f"ERROR: benchmark file not found: {args.benchmark}")
+
+    try:
+        sequences = load_sequences(args.benchmark)
+    except Exception as exc:
+        sys.exit(f"ERROR: failed to parse benchmark inputs: {exc}")
+
+    if args.sequences:
+        keep = set(args.sequences)
+        sequences = [seq for seq in sequences if seq["seq_id"] in keep]
+
+    os.makedirs(args.output_dir, exist_ok=True)
+    gt_requests_path = os.path.join(args.output_dir, "gt_requests.json")
+
+    print(f"ffmpeg:     {ffmpeg}")
+    print(f"benchmark:  {args.benchmark}")
+    print(f"repo:       {args.repo_id}")
+    print(f"subdir:     {args.repo_subdir}")
+    print(f"nframes:    {args.nframes}")
+    print(f"stride:     {'auto' if args.stride is None else args.stride}")
+    print(f"sequences:  {len(sequences)}")
+
+    video_groups: Dict[tuple, List[dict]] = {}
+    for seq in sequences:
+        key = (get_split(seq["scene"]), seq["scene"], seq["camera"])
+        video_groups.setdefault(key, []).append(seq)
+
+    print(f"videos:     {len(video_groups)} unique source camera files\n")
+
+    done = 0
+    skipped = 0
+    errors = 0
+    video_cache: Dict[tuple, str] = {}
+    gt_requests: List[dict] = []
+    write_gt_requests(
+        gt_requests_path=gt_requests_path,
+        benchmark_path=args.benchmark,
+        repo_id=args.repo_id,
+        repo_subdir=args.repo_subdir,
+        nframes=args.nframes,
+        stride=args.stride,
+        requests=gt_requests,
+    )
+
+    for (split, scene, camera), seqs in sorted(video_groups.items()):
+        video_key = (split, scene, camera)
+        if video_key not in video_cache:
+            rel_path = f"{args.repo_subdir}/{split}/{scene}/videos/{camera}.mp4"
+            print(f"[VIDEO] {rel_path}  ({len(seqs)} sequence{'s' if len(seqs) != 1 else ''})")
+            try:
+                video_cache[video_key] = download_hf_file_with_retries(
+                    repo_id=args.repo_id,
+                    filename=rel_path,
+                    hf_token=hf_token,
+                    hf_cache_dir=args.hf_cache_dir,
+                )
+                print(f"        -> {video_cache[video_key]}")
+            except Exception as exc:
+                print(f"  [DOWNLOAD ERROR] {exc}")
+                errors += len(seqs)
+                continue
+
+        video_path = video_cache[video_key]
+
+        for seq in seqs:
+            seq_id = seq["seq_id"]
+            seq_out = os.path.join(args.output_dir, seq_id)
+            frames_out = os.path.join(seq_out, "frames")
+
+            if existing_frame_count(frames_out) >= args.nframes:
+                skipped += 1
+                continue
+
+            os.makedirs(frames_out, exist_ok=True)
+
+            canonical = seq.get("canonical_frame_ids") or []
+            if canonical and args.stride is None and len(canonical) >= args.nframes:
+                frame_ids = canonical[: args.nframes]
+                stride = (
+                    canonical[1] - canonical[0]
+                    if len(canonical) > 1
+                    else DEFAULT_STRIDE
+                )
+            else:
+                stride = choose_stride(
+                    n_frames=args.nframes,
+                    init_frame_id=seq["init_frame_id"],
+                    clip_end_frame=seq.get("clip_end_frame"),
+                    user_stride=args.stride,
+                )
+                frame_ids = [
+                    seq["init_frame_id"] + idx * stride for idx in range(args.nframes)
+                ]
+
+            gt_request_frames = [
+                {
+                    "frame_index": frame_idx,
+                    "source_frame_id": source_frame_id,
+                }
+                for frame_idx, source_frame_id in enumerate(frame_ids)
+            ]
+            gt_request_entry = {
+                "seq_id": seq_id,
+                "scene": seq["scene"],
+                "camera": seq["camera"],
+                "object_id": seq["object_id"],
+                "object_type": seq["object_type"],
+                "label": sequence_label(seq),
+                "requested_nframes": len(frame_ids),
+                "requested_stride": stride,
+                "frame_requests": gt_request_frames,
+                "status": "pending_preparation",
+            }
+            gt_requests.append(gt_request_entry)
+            write_gt_requests(
+                gt_requests_path=gt_requests_path,
+                benchmark_path=args.benchmark,
+                repo_id=args.repo_id,
+                repo_subdir=args.repo_subdir,
+                nframes=args.nframes,
+                stride=args.stride,
+                requests=gt_requests,
+            )
+
+            try:
+                for frame_idx, source_frame_id in enumerate(frame_ids):
+                    timestamp_sec = source_frame_id / seq["clip_fps"]
+                    frame_path = os.path.join(frames_out, f"f{frame_idx:02d}.png")
+                    extract_frame(ffmpeg, video_path, timestamp_sec, frame_path)
+
+                    if frame_idx == 0:
+                        draw_bbox_on_frame(
+                            frame_path,
+                            seq["init_bbox"],
+                            os.path.join(frames_out, "f00_ann.png"),
+                        )
+                        crop_target(
+                            frame_path,
+                            seq["init_bbox"],
+                            os.path.join(frames_out, "crop.png"),
+                        )
+
+                sequence_meta = {
+                    "frame_ids": frame_ids,
+                    "init_bbox": seq["init_bbox"],
+                    "label": sequence_label(seq),
+                    "scene": seq["scene"],
+                    "camera": seq["camera"],
+                    "object_id": seq["object_id"],
+                    "object_type": seq["object_type"],
+                    "stride": stride,
+                    "nframes": len(frame_ids),
+                    "clip_fps": seq["clip_fps"],
+                    "gt_available": False,
+                    "gt_source": "private_benchmark_owner_only",
+                }
+                with open(os.path.join(seq_out, "sequence_meta.json"), "w", encoding="utf-8") as f:
+                    json.dump(sequence_meta, f, indent=2)
+
+                gt_request_entry["status"] = "prepared"
+                write_gt_requests(
+                    gt_requests_path=gt_requests_path,
+                    benchmark_path=args.benchmark,
+                    repo_id=args.repo_id,
+                    repo_subdir=args.repo_subdir,
+                    nframes=args.nframes,
+                    stride=args.stride,
+                    requests=gt_requests,
+                )
+
+                print(
+                    f"  [{done + 1:>3}] {seq_id}  stride={stride}  "
+                    f"gt=private"
+                )
+                done += 1
+            except Exception as exc:
+                gt_request_entry["status"] = "preparation_failed"
+                gt_request_entry["error"] = str(exc)
+                write_gt_requests(
+                    gt_requests_path=gt_requests_path,
+                    benchmark_path=args.benchmark,
+                    repo_id=args.repo_id,
+                    repo_subdir=args.repo_subdir,
+                    nframes=args.nframes,
+                    stride=args.stride,
+                    requests=gt_requests,
+                )
+                print(f"  [ERROR] {seq_id}: {exc}")
+                errors += 1
+
+    write_gt_requests(
+        gt_requests_path=gt_requests_path,
+        benchmark_path=args.benchmark,
+        repo_id=args.repo_id,
+        repo_subdir=args.repo_subdir,
+        nframes=args.nframes,
+        stride=args.stride,
+        requests=gt_requests,
+    )
+
+    print(f"\n{'=' * 60}")
+    print(f"Done:    {done} prepared")
+    print(f"Skipped: {skipped} already complete")
+    print(f"Errors:  {errors}")
+    print(f"Output:  {args.output_dir}")
+    print(f"GT reqs:  {gt_requests_path}")
+    print(f'\nSet "prepared_data_dir": "{args.output_dir}" in your config JSON.')
+
+
+if __name__ == "__main__":
+    main()
diff --git a/data/tracking/sot_benchmark.jsonl b/data/tracking/sot_benchmark.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c1f9e5a40be5a4f6b37750c67f62dcfc22aa7545
--- /dev/null
+++ b/data/tracking/sot_benchmark.jsonl
@@ -0,0 +1,200 @@
+{"seq_id": "Warehouse_016__Camera_11__5600__obj353", "scene": "Warehouse_016", "camera": "Camera_11", "object_id": "353", "object_type": "Robot", "init_bbox": [799.0, 601.9, 918.8, 956.5], "init_frame_id": 5600, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [5600, 5615, 5630, 5645, 5660, 5675, 5690, 5705]}
+{"seq_id": "Warehouse_016__Camera_05__704__obj352", "scene": "Warehouse_016", "camera": "Camera_05", "object_id": "352", "object_type": "Robot", "init_bbox": [819.8, 675.9, 894.8, 958.3], "init_frame_id": 704, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [704, 719, 734, 749, 764, 779, 794, 809]}
+{"seq_id": "Warehouse_016__Camera_11__5040__obj353", "scene": "Warehouse_016", "camera": "Camera_11", "object_id": "353", "object_type": "Robot", "init_bbox": [284.4, 559.3, 375.0, 961.1], "init_frame_id": 5040, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [5040, 5055, 5070, 5085, 5100, 5115, 5130, 5145]}
+{"seq_id": "Warehouse_016__Camera_05__1968__obj353", "scene": "Warehouse_016", "camera": "Camera_05", "object_id": "353", "object_type": "Robot", "init_bbox": [865.6, 683.3, 948.4, 963.0], "init_frame_id": 1968, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [1968, 1983, 1998, 2013, 2028, 2043, 2058, 2073]}
+{"seq_id": "Warehouse_014__Camera_01__4192__obj528", "scene": "Warehouse_014", "camera": "Camera_01", "object_id": "528", "object_type": "Robot", "init_bbox": [810.9, 736.1, 931.8, 969.4], "init_frame_id": 4192, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [4192, 4207, 4222, 4237, 4252, 4267, 4282, 4297]}
+{"seq_id": "Warehouse_014__Camera_01__7792__obj528", "scene": "Warehouse_014", "camera": "Camera_01", "object_id": "528", "object_type": "Robot", "init_bbox": [809.9, 731.5, 930.2, 963.9], "init_frame_id": 7792, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [7792, 7807, 7822, 7837, 7852, 7867, 7882, 7897]}
+{"seq_id": "Warehouse_016__Camera_06__8509__obj627", "scene": "Warehouse_016", "camera": "Camera_06", "object_id": "627", "object_type": "Robot", "init_bbox": [210.4, 632.4, 287.5, 941.7], "init_frame_id": 8509, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [8509, 8524, 8539, 8554, 8569, 8584, 8599, 8614]}
+{"seq_id": "Warehouse_016__Camera_02__4416__obj354", "scene": "Warehouse_016", "camera": "Camera_02", "object_id": "354", "object_type": "Robot", "init_bbox": [75.0, 723.1, 175.5, 874.1], "init_frame_id": 4416, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [4416, 4431, 4446, 4461, 4476, 4491, 4506, 4521]}
+{"seq_id": "Warehouse_016__Camera__2976__obj352", "scene": "Warehouse_016", "camera": "Camera", "object_id": "352", "object_type": "Robot", "init_bbox": [30.2, 679.6, 146.9, 850.0], "init_frame_id": 2976, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [2976, 2991, 3006, 3021, 3036, 3051, 3066, 3081]}
+{"seq_id": "Warehouse_016__Camera_03__2608__obj352", "scene": "Warehouse_016", "camera": "Camera_03", "object_id": "352", "object_type": "Robot", "init_bbox": [248.4, 397.2, 315.1, 688.9], "init_frame_id": 2608, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [2608, 2623, 2638, 2653, 2668, 2683, 2698, 2713]}
+{"seq_id": "Warehouse_016__Camera_03__544__obj352", "scene": "Warehouse_016", "camera": "Camera_03", "object_id": "352", "object_type": "Robot", "init_bbox": [774.5, 384.3, 853.1, 653.7], "init_frame_id": 544, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [544, 559, 574, 589, 604, 619, 634, 649]}
+{"seq_id": "Warehouse_016__Camera_11__6688__obj353", "scene": "Warehouse_016", "camera": "Camera_11", "object_id": "353", "object_type": "Robot", "init_bbox": [625.5, 587.0, 732.3, 955.6], "init_frame_id": 6688, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [6688, 6703, 6718, 6733, 6748, 6763, 6778, 6793]}
+{"seq_id": "Warehouse_016__Camera_03__1728__obj352", "scene": "Warehouse_016", "camera": "Camera_03", "object_id": "352", "object_type": "Robot", "init_bbox": [302.1, 466.7, 372.9, 760.2], "init_frame_id": 1728, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [1728, 1743, 1758, 1773, 1788, 1803, 1818, 1833]}
+{"seq_id": "Warehouse_016__Camera_07__4352__obj353", "scene": "Warehouse_016", "camera": "Camera_07", "object_id": "353", "object_type": "Robot", "init_bbox": [318.2, 546.3, 389.1, 825.0], "init_frame_id": 4352, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [4352, 4367, 4382, 4397, 4412, 4427, 4442, 4457]}
+{"seq_id": "Warehouse_016__Camera__7792__obj627", "scene": "Warehouse_016", "camera": "Camera", "object_id": "627", "object_type": "Robot", "init_bbox": [125.5, 456.5, 217.2, 617.6], "init_frame_id": 7792, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [7792, 7807, 7822, 7837, 7852, 7867, 7882, 7897]}
+{"seq_id": "Warehouse_016__Camera_08__1760__obj352", "scene": "Warehouse_016", "camera": "Camera_08", "object_id": "352", "object_type": "Robot", "init_bbox": [751.0, 590.7, 842.7, 911.1], "init_frame_id": 1760, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [1760, 1775, 1790, 1805, 1820, 1835, 1850, 1865]}
+{"seq_id": "Warehouse_016__Camera_04__2640__obj352", "scene": "Warehouse_016", "camera": "Camera_04", "object_id": "352", "object_type": "Robot", "init_bbox": [43.8, 682.4, 120.3, 935.2], "init_frame_id": 2640, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [2640, 2655, 2670, 2685, 2700, 2715, 2730, 2745]}
+{"seq_id": "Warehouse_005__Camera_0016__400__obj31", "scene": "Warehouse_005", "camera": "Camera_0016", "object_id": "31", "object_type": "Robot", "init_bbox": [102.1, 782.4, 346.9, 958.3], "init_frame_id": 400, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [400, 415, 430, 445, 460, 475, 490, 505]}
+{"seq_id": "Warehouse_000__Camera_0015__944__obj27", "scene": "Warehouse_000", "camera": "Camera_0015", "object_id": "27", "object_type": "Robot", "init_bbox": [215.6, 707.4, 357.3, 867.6], "init_frame_id": 944, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [944, 959, 974, 989, 1004, 1019, 1034, 1049]}
+{"seq_id": "Warehouse_016__Camera_07__6400__obj353", "scene": "Warehouse_016", "camera": "Camera_07", "object_id": "353", "object_type": "Robot", "init_bbox": [300.0, 400.0, 349.0, 614.8], "init_frame_id": 6400, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [6400, 6415, 6430, 6445, 6460, 6475, 6490, 6505]}
+{"seq_id": "Warehouse_005__Camera_0000__3408__obj31", "scene": "Warehouse_005", "camera": "Camera_0000", "object_id": "31", "object_type": "Robot", "init_bbox": [41.1, 669.4, 286.5, 838.9], "init_frame_id": 3408, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [3408, 3423, 3438, 3453, 3468, 3483, 3498, 3513]}
+{"seq_id": "Warehouse_012__Camera_08__1776__obj313", "scene": "Warehouse_012", "camera": "Camera_08", "object_id": "313", "object_type": "Robot", "init_bbox": [283.3, 825.0, 378.1, 967.6], "init_frame_id": 1776, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [1776, 1791, 1806, 1821, 1836, 1851, 1866, 1881]}
+{"seq_id": "Warehouse_016__Camera_05__1744__obj352", "scene": "Warehouse_016", "camera": "Camera_05", "object_id": "352", "object_type": "Robot", "init_bbox": [389.1, 605.6, 464.6, 875.0], "init_frame_id": 1744, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [1744, 1759, 1774, 1789, 1804, 1819, 1834, 1849]}
+{"seq_id": "Warehouse_016__Camera_04__7760__obj627", "scene": "Warehouse_016", "camera": "Camera_04", "object_id": "627", "object_type": "Robot", "init_bbox": [67.2, 425.0, 122.9, 633.3], "init_frame_id": 7760, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [7760, 7775, 7790, 7805, 7820, 7835, 7850, 7865]}
+{"seq_id": "Warehouse_016__Camera_03__5616__obj353", "scene": "Warehouse_016", "camera": "Camera_03", "object_id": "353", "object_type": "Robot", "init_bbox": [166.7, 137.0, 226.6, 369.4], "init_frame_id": 5616, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [5616, 5631, 5646, 5661, 5676, 5691, 5706, 5721]}
+{"seq_id": "Warehouse_000__Camera_0016__1152__obj22", "scene": "Warehouse_000", "camera": "Camera_0016", "object_id": "22", "object_type": "Robot", "init_bbox": [308.9, 722.2, 522.9, 951.9], "init_frame_id": 1152, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [1152, 1167, 1182, 1197, 1212, 1227, 1242, 1257]}
+{"seq_id": "Warehouse_016__Camera_08__592__obj353", "scene": "Warehouse_016", "camera": "Camera_08", "object_id": "353", "object_type": "Robot", "init_bbox": [741.1, 379.6, 814.6, 650.0], "init_frame_id": 592, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [592, 607, 622, 637, 652, 667, 682, 697]}
+{"seq_id": "Warehouse_016__Camera_09__5008__obj353", "scene": "Warehouse_016", "camera": "Camera_09", "object_id": "353", "object_type": "Robot", "init_bbox": [803.6, 93.5, 838.5, 189.8], "init_frame_id": 5008, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [5008, 5023, 5038, 5053, 5068, 5083, 5098, 5113]}
+{"seq_id": "Warehouse_000__Camera_0016__1680__obj22", "scene": "Warehouse_000", "camera": "Camera_0016", "object_id": "22", "object_type": "Robot", "init_bbox": [281.8, 582.4, 449.0, 651.9], "init_frame_id": 1680, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [1680, 1695, 1710, 1725, 1740, 1755, 1770, 1785]}
+{"seq_id": "Warehouse_014__Camera__48__obj466", "scene": "Warehouse_014", "camera": "Camera", "object_id": "466", "object_type": "Robot", "init_bbox": [793.8, 597.2, 890.1, 786.1], "init_frame_id": 48, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [48, 63, 78, 93, 108, 123, 138, 153]}
+{"seq_id": "Warehouse_014__Camera_02__480__obj416", "scene": "Warehouse_014", "camera": "Camera_02", "object_id": "416", "object_type": "Robot", "init_bbox": [801.0, 396.3, 866.1, 480.6], "init_frame_id": 480, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [480, 495, 510, 525, 540, 555, 570, 585]}
+{"seq_id": "Warehouse_014__Camera__5712__obj466", "scene": "Warehouse_014", "camera": "Camera", "object_id": "466", "object_type": "Robot", "init_bbox": [779.2, 547.2, 861.5, 735.2], "init_frame_id": 5712, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [5712, 5727, 5742, 5757, 5772, 5787, 5802, 5817]}
+{"seq_id": "Warehouse_015__Camera_10__3040__obj7", "scene": "Warehouse_015", "camera": "Camera_10", "object_id": "7", "object_type": "Robot", "init_bbox": [186.5, 648.1, 366.1, 794.4], "init_frame_id": 3040, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [3040, 3055, 3070, 3085, 3100, 3115, 3130, 3145]}
+{"seq_id": "Warehouse_013__Camera_06__8200__obj349", "scene": "Warehouse_013", "camera": "Camera_06", "object_id": "349", "object_type": "Robot", "init_bbox": [287.5, 748.1, 364.6, 887.0], "init_frame_id": 8200, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [8200, 8215, 8230, 8245, 8260, 8275, 8290, 8305]}
+{"seq_id": "Warehouse_014__Camera__3776__obj466", "scene": "Warehouse_014", "camera": "Camera", "object_id": "466", "object_type": "Robot", "init_bbox": [780.7, 600.9, 887.0, 764.8], "init_frame_id": 3776, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [3776, 3791, 3806, 3821, 3836, 3851, 3866, 3881]}
+{"seq_id": "Warehouse_014__Camera_03__5120__obj246", "scene": "Warehouse_014", "camera": "Camera_03", "object_id": "246", "object_type": "Robot", "init_bbox": [446.4, 195.4, 503.6, 449.1], "init_frame_id": 5120, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [5120, 5135, 5150, 5165, 5180, 5195, 5210, 5225]}
+{"seq_id": "Warehouse_014__Camera__5664__obj466", "scene": "Warehouse_014", "camera": "Camera", "object_id": "466", "object_type": "Robot", "init_bbox": [784.4, 593.5, 883.3, 767.6], "init_frame_id": 5664, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [5664, 5679, 5694, 5709, 5724, 5739, 5754, 5769]}
+{"seq_id": "Warehouse_016__Camera_08__4146__obj353", "scene": "Warehouse_016", "camera": "Camera_08", "object_id": "353", "object_type": "Robot", "init_bbox": [704.2, 337.0, 779.2, 589.8], "init_frame_id": 4146, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [4146, 4161, 4176, 4191, 4206, 4221, 4236, 4251]}
+{"seq_id": "Warehouse_005__Camera_0016__4832__obj31", "scene": "Warehouse_005", "camera": "Camera_0016", "object_id": "31", "object_type": "Robot", "init_bbox": [369.8, 529.6, 527.1, 619.4], "init_frame_id": 4832, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [4832, 4847, 4862, 4877, 4892, 4907, 4922, 4937]}
+{"seq_id": "Warehouse_014__Camera__7536__obj466", "scene": "Warehouse_014", "camera": "Camera", "object_id": "466", "object_type": "Robot", "init_bbox": [781.8, 599.1, 889.6, 755.6], "init_frame_id": 7536, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [7536, 7551, 7566, 7581, 7596, 7611, 7626, 7641]}
+{"seq_id": "Warehouse_014__Camera_03__6832__obj246", "scene": "Warehouse_014", "camera": "Camera_03", "object_id": "246", "object_type": "Robot", "init_bbox": [445.8, 193.5, 503.6, 450.0], "init_frame_id": 6832, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [6832, 6847, 6862, 6877, 6892, 6907, 6922, 6937]}
+{"seq_id": "Warehouse_016__Camera_08__320__obj354", "scene": "Warehouse_016", "camera": "Camera_08", "object_id": "354", "object_type": "Robot", "init_bbox": [906.8, 216.7, 965.6, 448.1], "init_frame_id": 320, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [320, 335, 350, 365, 380, 395, 410, 425]}
+{"seq_id": "Warehouse_002__Camera_0028__3808__obj48", "scene": "Warehouse_002", "camera": "Camera_0028", "object_id": "48", "object_type": "Robot", "init_bbox": [289.1, 625.0, 422.9, 688.0], "init_frame_id": 3808, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [3808, 3823, 3838, 3853, 3868, 3883, 3898, 3913]}
+{"seq_id": "Warehouse_004__Camera_0008__5392__obj1", "scene": "Warehouse_004", "camera": "Camera_0008", "object_id": "1", "object_type": "Robot", "init_bbox": [388.0, 515.7, 516.1, 584.3], "init_frame_id": 5392, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [5392, 5407, 5422, 5437, 5452, 5467, 5482, 5497]}
+{"seq_id": "Warehouse_013__Camera_08__288__obj0", "scene": "Warehouse_013", "camera": "Camera_08", "object_id": "0", "object_type": "Robot", "init_bbox": [684.4, 714.8, 762.0, 838.9], "init_frame_id": 288, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [288, 303, 318, 333, 348, 363, 378, 393]}
+{"seq_id": "Warehouse_012__Camera_06__8496__obj449", "scene": "Warehouse_012", "camera": "Camera_06", "object_id": "449", "object_type": "Robot", "init_bbox": [327.1, 798.1, 377.6, 946.3], "init_frame_id": 8496, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [8496, 8511, 8526, 8541, 8556, 8571, 8586, 8601]}
+{"seq_id": "Warehouse_016__Camera__8096__obj352", "scene": "Warehouse_016", "camera": "Camera", "object_id": "352", "object_type": "Robot", "init_bbox": [38.0, 315.7, 138.5, 481.5], "init_frame_id": 8096, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [8096, 8111, 8126, 8141, 8156, 8171, 8186, 8201]}
+{"seq_id": "Warehouse_014__Camera__7984__obj246", "scene": "Warehouse_014", "camera": "Camera", "object_id": "246", "object_type": "Robot", "init_bbox": [59.4, 356.5, 179.2, 517.6], "init_frame_id": 7984, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [7984, 7999, 8014, 8029, 8044, 8059, 8074, 8089]}
+{"seq_id": "Warehouse_014__Camera_01__4736__obj528", "scene": "Warehouse_014", "camera": "Camera_01", "object_id": "528", "object_type": "Robot", "init_bbox": [575.5, 447.2, 669.8, 508.3], "init_frame_id": 4736, "clip_fps": 30.0, "img_width": 1920, "img_height": 1080, "canonical_frame_ids": [4736, 4751, 4766, 4781, 4796, 4811, 4826, 4841]}
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