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Upload dataset files (batch 158/552)

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  1. .gitattributes +272 -0
  2. train/FloorPlan403/0/0.json +1167 -0
  3. train/FloorPlan403/0/0_depth_npy.npy +3 -0
  4. train/FloorPlan403/0/0_semantic_sensor_npy.npy +3 -0
  5. train/FloorPlan403/0/1.json +1231 -0
  6. train/FloorPlan403/0/1_depth_npy.npy +3 -0
  7. train/FloorPlan403/0/1_semantic_sensor_npy.npy +3 -0
  8. train/FloorPlan403/0/depth_0.png +0 -0
  9. train/FloorPlan403/0/depth_1.png +0 -0
  10. train/FloorPlan403/0/mask_questions.jsonl +5 -0
  11. train/FloorPlan403/0/questions_distance.jsonl +2 -0
  12. train/FloorPlan403/0/rgb_0.png +3 -0
  13. train/FloorPlan403/0/rgb_0_mask_Faucet|-01.28|+00.59|+00.63.png +3 -0
  14. train/FloorPlan403/0/rgb_0_mask_Window|-02.19|+01.55|+00.09.png +3 -0
  15. train/FloorPlan403/0/rgb_1.png +3 -0
  16. train/FloorPlan403/0/rgb_1_mask_Faucet|-01.28|+00.59|+00.63.png +3 -0
  17. train/FloorPlan403/0/rgb_1_mask_Toilet|-00.70|+00.03|+00.66.png +3 -0
  18. train/FloorPlan403/0/rgb_1_mask_Window|-02.19|+01.55|+00.09.png +3 -0
  19. train/FloorPlan403/0/semantic_0.png +0 -0
  20. train/FloorPlan403/0/semantic_1.png +0 -0
  21. train/FloorPlan403/1/0.json +1231 -0
  22. train/FloorPlan403/1/0_depth_npy.npy +3 -0
  23. train/FloorPlan403/1/0_semantic_sensor_npy.npy +3 -0
  24. train/FloorPlan403/1/1.json +941 -0
  25. train/FloorPlan403/1/1_depth_npy.npy +3 -0
  26. train/FloorPlan403/1/1_semantic_sensor_npy.npy +3 -0
  27. train/FloorPlan403/1/depth_0.png +0 -0
  28. train/FloorPlan403/1/depth_1.png +0 -0
  29. train/FloorPlan403/1/mask_questions.jsonl +2 -0
  30. train/FloorPlan403/1/questions_distance.jsonl +1 -0
  31. train/FloorPlan403/1/rgb_0.png +3 -0
  32. train/FloorPlan403/1/rgb_0_mask_Toilet|-00.70|+00.03|+00.66.png +3 -0
  33. train/FloorPlan403/1/rgb_1.png +3 -0
  34. train/FloorPlan403/1/rgb_1_mask_Toilet|-00.70|+00.03|+00.66.png +3 -0
  35. train/FloorPlan403/1/semantic_0.png +0 -0
  36. train/FloorPlan403/1/semantic_1.png +0 -0
  37. train/FloorPlan403/10/0.json +2275 -0
  38. train/FloorPlan403/10/0_depth_npy.npy +3 -0
  39. train/FloorPlan403/10/0_semantic_sensor_npy.npy +3 -0
  40. train/FloorPlan403/10/1.json +1167 -0
  41. train/FloorPlan403/10/1_depth_npy.npy +3 -0
  42. train/FloorPlan403/10/1_semantic_sensor_npy.npy +3 -0
  43. train/FloorPlan403/10/depth_0.png +0 -0
  44. train/FloorPlan403/10/depth_1.png +0 -0
  45. train/FloorPlan403/10/mask_questions.jsonl +2 -0
  46. train/FloorPlan403/10/rgb_0.png +3 -0
  47. train/FloorPlan403/10/rgb_0_mask_Faucet|-01.28|+00.59|+00.63.png +3 -0
  48. train/FloorPlan403/10/rgb_1.png +3 -0
  49. train/FloorPlan403/10/rgb_1_mask_Window|-02.19|+01.55|+00.09.png +3 -0
  50. train/FloorPlan403/10/semantic_0.png +0 -0
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+ {"id": 8674, "folder_dir": "FloorPlan403/0", "pair_name": "0-1:Window|-02.19|+01.55|+00.09", "query": "Please combine the information from the two images and answer the following question:\nWhat is the object being masked in the second image?\nNote: The masked region is indicated by the black rectangle outlined in red in the second image.\nChoose the correct option from the list below. Only respond with the corresponding letter.\n\nA. Bathtub\nB. Window\nC. ScrubBrush\nD. Plunger", "answer": "B", "object_name": "Window", "image_1": "FloorPlan403/0/rgb_0.png", "image_2": "FloorPlan403/0/rgb_1.png", "mask_image": "FloorPlan403/0/rgb_1_mask_Window|-02.19|+01.55|+00.09.png", "options": ["Bathtub", "Window", "ScrubBrush", "Plunger"]}
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+ {"id": 8675, "folder_dir": "FloorPlan403/0", "pair_name": "0-1:Faucet|-01.28|+00.59|+00.63", "query": "Please combine the information from the two images and answer the following question:\nWhat is the object being masked in the second image?\nNote: The masked region is indicated by the black rectangle outlined in red in the second image.\nChoose the correct option from the list below. Only respond with the corresponding letter.\n\nA. Bathtub\nB. Faucet\nC. Plunger\nD. ScrubBrush", "answer": "B", "object_name": "Faucet", "image_1": "FloorPlan403/0/rgb_0.png", "image_2": "FloorPlan403/0/rgb_1.png", "mask_image": "FloorPlan403/0/rgb_1_mask_Faucet|-01.28|+00.59|+00.63.png", "options": ["Bathtub", "Faucet", "Plunger", "ScrubBrush"]}
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+ {"id": 8676, "folder_dir": "FloorPlan403/0", "pair_name": "0-1:Toilet|-00.70|+00.03|+00.66", "query": "Please combine the information from the two images and answer the following question:\nWhat is the object being masked in the second image?\nNote: The masked region is indicated by the black rectangle outlined in red in the second image.\nChoose the correct option from the list below. Only respond with the corresponding letter.\n\nA. Bathtub\nB. ToiletPaper\nC. Toilet\nD. ToiletPaperHanger", "answer": "C", "object_name": "Toilet", "image_1": "FloorPlan403/0/rgb_0.png", "image_2": "FloorPlan403/0/rgb_1.png", "mask_image": "FloorPlan403/0/rgb_1_mask_Toilet|-00.70|+00.03|+00.66.png", "options": ["Bathtub", "ToiletPaper", "Toilet", "ToiletPaperHanger"]}
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+ {"id": 8677, "folder_dir": "FloorPlan403/0", "pair_name": "1-0:Window|-02.19|+01.55|+00.09", "query": "Please combine the information from the two images and answer the following question:\nWhat is the object being masked in the second image?\nNote: The masked region is indicated by the black rectangle outlined in red in the second image.\nChoose the correct option from the list below. Only respond with the corresponding letter.\n\nA. Bathtub\nB. Window\nC. Candle\nD. Cloth", "answer": "B", "object_name": "Window", "image_1": "FloorPlan403/0/rgb_1.png", "image_2": "FloorPlan403/0/rgb_0.png", "mask_image": "FloorPlan403/0/rgb_0_mask_Window|-02.19|+01.55|+00.09.png", "options": ["Bathtub", "Window", "Candle", "Cloth"]}
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+ {"id": 2600, "folder_dir": "FloorPlan403/0", "query": "Which of the following objects is closest to Faucet in the two images? (Note: Use the distance between centroids to determine closeness.) \nChoose the correct option from the list below. Only respond with the corresponding letter.\n\nA. Cloth\nB. Toilet\nC. Candle\nD. Other\n", "answer": "C", "src_object": "Faucet", "target_object": "Candle", "question": "Which of the following objects is closest to Faucet in the two images? (Note: Use the distance between centroids to determine closeness.) \nChoose the correct option from the list below. Only respond with the corresponding letter.", "options": [{"text": "Cloth", "semantic_id": "Cloth|-01.64|+00.59|+01.11", "distance": 0.5316042464796944, "com": {"x": -1.6378650665283203, "y": 0.6183820962905884, "z": 1.119027853012085}}, {"text": "Toilet", "semantic_id": "Toilet|-00.70|+00.03|+00.66", "distance": 0.7442039458272303, "com": {"x": -0.6948426365852356, "y": 0.5434154868125916, "z": 0.6699745655059814}}, {"text": "Candle", "semantic_id": "Candle|-01.30|+00.59|+01.06", "distance": 0.4524591238768686, "com": {"x": -1.29707932472229, "y": 0.6524348258972168, "z": 1.0612859725952148}}, {"text": "Other", "semantic_id": -1, "distance": -1, "com": [0, 0, 0]}], "image_1": "FloorPlan403/0/rgb_0.png", "image_2": "FloorPlan403/0/rgb_1.png"}
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