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Upload dataset files (batch 43/552)

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  1. .gitattributes +299 -0
  2. train/FloorPlan29/0/0.json +0 -0
  3. train/FloorPlan29/0/0_depth_npy.npy +3 -0
  4. train/FloorPlan29/0/0_semantic_sensor_npy.npy +3 -0
  5. train/FloorPlan29/0/1.json +0 -0
  6. train/FloorPlan29/0/1_depth_npy.npy +3 -0
  7. train/FloorPlan29/0/1_semantic_sensor_npy.npy +3 -0
  8. train/FloorPlan29/0/depth_0.png +0 -0
  9. train/FloorPlan29/0/depth_1.png +0 -0
  10. train/FloorPlan29/0/mask_questions.jsonl +3 -0
  11. train/FloorPlan29/0/questions_distance.jsonl +3 -0
  12. train/FloorPlan29/0/rgb_0.png +3 -0
  13. train/FloorPlan29/0/rgb_0_mask_Fridge|-01.29|+00.02|+01.83.png +3 -0
  14. train/FloorPlan29/0/rgb_0_mask_Lettuce|+00.89|+01.10|+00.13.png +3 -0
  15. train/FloorPlan29/0/rgb_1.png +3 -0
  16. train/FloorPlan29/0/rgb_1_mask_Lettuce|+00.89|+01.10|+00.13.png +3 -0
  17. train/FloorPlan29/0/semantic_0.png +0 -0
  18. train/FloorPlan29/0/semantic_1.png +0 -0
  19. train/FloorPlan29/1/0.json +0 -0
  20. train/FloorPlan29/1/0_depth_npy.npy +3 -0
  21. train/FloorPlan29/1/0_semantic_sensor_npy.npy +3 -0
  22. train/FloorPlan29/1/1.json +1178 -0
  23. train/FloorPlan29/1/1_depth_npy.npy +3 -0
  24. train/FloorPlan29/1/1_semantic_sensor_npy.npy +3 -0
  25. train/FloorPlan29/1/depth_0.png +0 -0
  26. train/FloorPlan29/1/depth_1.png +0 -0
  27. train/FloorPlan29/1/mask_questions.jsonl +6 -0
  28. train/FloorPlan29/1/rgb_0.png +3 -0
  29. train/FloorPlan29/1/rgb_0_mask_Cabinet|+01.99|+00.59|-01.11.png +3 -0
  30. train/FloorPlan29/1/rgb_0_mask_Cabinet|+01.99|+01.40|-01.11.png +3 -0
  31. train/FloorPlan29/1/rgb_0_mask_CoffeeMachine|+01.93|+01.03|+00.44.png +3 -0
  32. train/FloorPlan29/1/rgb_1.png +3 -0
  33. train/FloorPlan29/1/rgb_1_mask_Cabinet|+01.99|+00.59|-01.11.png +3 -0
  34. train/FloorPlan29/1/rgb_1_mask_Cabinet|+01.99|+01.40|-01.11.png +3 -0
  35. train/FloorPlan29/1/rgb_1_mask_CoffeeMachine|+01.93|+01.03|+00.44.png +3 -0
  36. train/FloorPlan29/1/semantic_0.png +0 -0
  37. train/FloorPlan29/1/semantic_1.png +0 -0
  38. train/FloorPlan29/10/0.json +0 -0
  39. train/FloorPlan29/10/0_depth_npy.npy +3 -0
  40. train/FloorPlan29/10/0_semantic_sensor_npy.npy +3 -0
  41. train/FloorPlan29/10/1.json +1387 -0
  42. train/FloorPlan29/10/1_depth_npy.npy +3 -0
  43. train/FloorPlan29/10/1_semantic_sensor_npy.npy +3 -0
  44. train/FloorPlan29/10/depth_0.png +0 -0
  45. train/FloorPlan29/10/depth_1.png +0 -0
  46. train/FloorPlan29/10/mask_questions.jsonl +6 -0
  47. train/FloorPlan29/10/rgb_0.png +3 -0
  48. train/FloorPlan29/10/rgb_0_mask_Cabinet|+01.32|+01.96|-01.31.png +3 -0
  49. train/FloorPlan29/10/rgb_0_mask_Cabinet|+01.99|+00.59|-01.11.png +3 -0
  50. train/FloorPlan29/10/rgb_0_mask_Cabinet|+01.99|+01.40|-01.11.png +3 -0
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+ {"id": 2304, "folder_dir": "FloorPlan29/0", "pair_name": "0-1:Lettuce|+00.89|+01.10|+00.13", "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. GarbageCan\nB. ButterKnife\nC. Lettuce\nD. Chair", "answer": "C", "object_name": "Lettuce", "image_1": "FloorPlan29/0/rgb_0.png", "image_2": "FloorPlan29/0/rgb_1.png", "mask_image": "FloorPlan29/0/rgb_1_mask_Lettuce|+00.89|+01.10|+00.13.png", "options": ["GarbageCan", "ButterKnife", "Lettuce", "Chair"]}
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