Tensorflow 对象检测 API 中未检测到任何内容 [英] Nothing is being detected in Tensorflow Object detection API
本文介绍了Tensorflow 对象检测 API 中未检测到任何内容的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我正在尝试实现 Tensorflow 对象检测 API 示例.我正在关注
另一个没有任何检测的图像.
我在这里缺少什么?代码包含在上面的链接中，没有错误日志.
box、score、classes、num 的结果按顺序排列.
[[[ 0.74907303 0.14624023 1. 1. ][ 0. 0. 1. 1. ][ 0. 0.20880508 1. 1. ][ 0.74907303 0.14624023 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0.74907303 0.14624023 1. 1. ][ 0.74907303 0.14624023 1. 1. ][ 0.74907303 0.14624023 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0.74907303 0.14624023 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0.74907303 0.14624023 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0.74907303 0.14624023 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0.20934391 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0.20880508 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0.74907303 0.14624023 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0.74907303 0.14624023 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0.74907303 0.14624023 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0.74907303 0.14624023 1. 1. ][ 0.74907303 0.14624023 1. 1. ][ 0.74907303 0.14624023 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0.74907303 0.14624023 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0.74907303 0.14624023 1. 1. ][ 0.74907303 0.14624023 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0.74907303 0.14624023 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ]]][[ 0.03587547 0.02224986 0.0186467 0.01096812 0.01003207 0.006544090.00633549 0.00534311 0.0049596 0.00410213 0.00362371 0.003391860.00308251 0.00303347 0.00293389 0.00277099 0.00269575 0.002668250.00263925 0.00263331 0.00258657 0.00240822 0.0022581 0.001869670.00184311 0.00180467 0.00177475 0.00173655 0.00172811 0.001719350.00171891 0.00170288 0.00163755 0.00162967 0.00160273 0.001565450.00153615 0.00140941 0.00132407 0.00131524 0.0013105 0.001294310.0012582 0.0012553 0.00122365 0.00119186 0.00115651 0.001151860.00112369 0.00107097 0.00105805 0.00104338 0.00102719 0.001023370.00100349 0.00097762 0.00096851 0.00092741 0.00088506 0.000876960.0008734 0.00084826 0.00084135 0.00083513 0.00083398 0.000820680.00080583 0.00078979 0.00078059 0.00077476 0.00075448 0.000744260.00074421 0.00070195 0.00068741 0.00068138 0.00067262 0.000671250.00067033 0.00066035 0.00064729 0.00064205 0.00061964 0.000617940.00060835 0.00060465 0.00059548 0.00059479 0.00059461 0.000594360.00059426 0.00059411 0.00059406 0.00059392 0.00059365 0.000593510.00059191 0.00058798 0.00058682 0.00058148]][[ 1. 1. 18. 32. 62. 60. 63. 67. 61. 49. 31. 84. 50. 54.15. 44. 44. 49. 31. 56. 88. 28. 88. 52. 17. 32. 38. 75.3. 33. 48. 59. 35. 57. 47. 51. 19. 27. 72. 4. 84. 6.55. 20. 58. 65. 61. 82. 42. 34. 40. 21. 43. 64. 39. 62.36. 22. 79. 46. 16. 40. 41. 77. 16. 48. 78. 77. 89. 86.27. 8. 87. 5. 25. 70. 80. 76. 75. 67. 65. 37. 2. 9.73. 63. 29. 30. 69. 66. 68. 26. 71. 12. 45. 83. 13. 85.74. 23.]][ 100.][[[0.0.1.1.][ 0. 0. 1. 1. ][ 0. 0.68494415 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0.68494415 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0.00784111 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0.68494415 1. 1. ][ 0. 0. 1. 1. ][ 0. 0.68494415 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0.68494415 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0.68494415 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0.68494415 1. 1. ][ 0. 0. 1. 1. ][ 0. 0.68494415 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0. 1. 1. ][ 0. 0.68494415 1. 1. ][0.0.68494415 1.1.]]][[ 0.01044297 0.0098214 0.00942165 0.00846471 0.00613666 0.003986150.00357754 0.0030054 0.00255861 0.00236574 0.00232631 0.002202910.00185227 0.0016354 0.0015979 0.00145072 0.00143661 0.001413690.00122685 0.00118978 0.00108457 0.00104251 0.00099215 0.000964010.0008708 0.00084773 0.00080484 0.00078507 0.00078378 0.000768760.00072774 0.00071732 0.00071348 0.00070812 0.00069253 0.00067620.00067269 0.00059905 0.00059367 0.000588 0.00056114 0.00055040.00051472 0.00051057 0.00050973 0.00048486 0.00047297 0.000462040.00044787 0.00043259 0.00042987 0.00042673 0.00041978 0.000404940.00040087 0.00039576 0.00039059 0.00037274 0.00036831 0.000364170.00036119 0.00034645 0.00034479 0.00034078 0.00033771 0.000336050.0003333 0.0003304 0.0003294 0.00032326 0.00031787 0.000317730.00031748 0.00031741 0.00031732 0.00031729 0.00031724 0.000317220.00031717 0.00031708 0.00031702 0.00031579 0.00030416 0.000302220.00029739 0.00029726 0.00028289 0.0002653 0.00026325 0.000245840.00024221 0.00024156 0.00023911 0.00023335 0.00021619 0.00020010.00019127 0.00018342 0.00017273 0.00015509]][[[ 38.1.1.16.25.38.64.24.49.56.20.3.28.2.48. 19. 21. 62. 50. 6. 8. 7. 67. 18. 35. 53. 39. 55.15. 57. 72. 52. 10. 5. 42. 43. 76. 22. 82. 4. 61. 23.17. 16. 87. 62. 51. 60. 36. 58. 59. 33. 31. 54. 70. 11.40. 79. 31. 9. 41. 77. 80. 34. 90. 89. 73. 13. 84. 32.63. 29. 30. 69. 66. 68. 26. 71. 12. 45. 83. 14. 44. 78.85. 46. 47. 19. 65. 74. 37. 27. 63. 88. 28. 81. 86. 75.27. 18.]][ 100.]根据建议的答案，当我们使用 faster_rcnn_resnet101_coco_2017_11_08 模型时，它是有效的.但它更准确，这就是为什么更慢.我希望这个应用程序高速运行，因为我将实时(在网络摄像头上)使用它进行对象检测.所以我需要使用更快的模型(ssd_mobilenet_v1_coco_2017_11_08)
解决方案
问题出在模型上:'ssd_mobilenet_v1_coco_2017_11_08'
解决方案:换一个不同的版本'ssd_mobilenet_v1_coco_11_06_2017' (这个模型类型是最快的，换其他模型类型会变慢而不是你想要的东西)
只需更改 1 行代码:
# 下载什么模型.MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'当我使用你的代码时，什么都没有显示，但是当我用我之前的实验模型替换它时'ssd_mobilenet_v1_coco_11_06_2017'它工作正常
I'm trying to implement Tensorflow object detection API sample. I am following sentdex videos for getting started. The sample code runs perfectly, it also shows the images which are used for testing the results, but no boundaries around detected objects are shown. Just the plane image is displayed without any errors.
I'm using this code: This Github link.
This is my result after running the sample code.
another image without any detection.
What I'm missing here? The code is included in above link and there is no error logs.
Results of box, score, classes, num in that order.
[[[ 0.74907303 0.14624023 1. 1. ]
[ 0. 0. 1. 1. ]
[ 0. 0.20880508 1. 1. ]
[ 0.74907303 0.14624023 1. 1. ]
[ 0. 0. 1. 1. ]
[ 0. 0. 1. 1. ]
[ 0. 0. 1. 1. ]
[ 0. 0. 1. 1. ]
[ 0. 0. 1. 1. ]
[ 0.74907303 0.14624023 1. 1. ]
[ 0.74907303 0.14624023 1. 1. ]
[ 0.74907303 0.14624023 1. 1. ]
[ 0. 0. 1. 1. ]
[ 0. 0. 1. 1. ]
[ 0. 0. 1. 1. ]
[ 0.74907303 0.14624023 1. 1. ]
[ 0. 0. 1. 1. ]
[ 0. 0. 1. 1. ]
[ 0. 0. 1. 1. ]
[ 0. 0. 1. 1. ]
[ 0.74907303 0.14624023 1. 1. ]
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[ 0.74907303 0.14624023 1. 1. ]
[ 0. 0. 1. 1. ]
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[ 0.74907303 0.14624023 1. 1. ]
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[ 0. 0. 1. 1. ]
[ 0. 0. 1. 1. ]
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[ 0.74907303 0.14624023 1. 1. ]
[ 0.74907303 0.14624023 1. 1. ]
[ 0.74907303 0.14624023 1. 1. ]
[ 0. 0. 1. 1. ]
[ 0. 0. 1. 1. ]
[ 0. 0. 1. 1. ]
[ 0. 0. 1. 1. ]
[ 0.74907303 0.14624023 1. 1. ]
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[ 0. 0. 1. 1. ]
[ 0. 0. 1. 1. ]
[ 0.74907303 0.14624023 1. 1. ]
[ 0.74907303 0.14624023 1. 1. ]
[ 0. 0. 1. 1. ]
[ 0. 0. 1. 1. ]
[ 0. 0. 1. 1. ]
[ 0. 0. 1. 1. ]
[ 0.74907303 0.14624023 1. 1. ]
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[ 0. 0. 1. 1. ]]]
[[ 0.03587547 0.02224986 0.0186467 0.01096812 0.01003207 0.00654409
0.00633549 0.00534311 0.0049596 0.00410213 0.00362371 0.00339186
0.00308251 0.00303347 0.00293389 0.00277099 0.00269575 0.00266825
0.00263925 0.00263331 0.00258657 0.00240822 0.0022581 0.00186967
0.00184311 0.00180467 0.00177475 0.00173655 0.00172811 0.00171935
0.00171891 0.00170288 0.00163755 0.00162967 0.00160273 0.00156545
0.00153615 0.00140941 0.00132407 0.00131524 0.0013105 0.00129431
0.0012582 0.0012553 0.00122365 0.00119186 0.00115651 0.00115186
0.00112369 0.00107097 0.00105805 0.00104338 0.00102719 0.00102337
0.00100349 0.00097762 0.00096851 0.00092741 0.00088506 0.00087696
0.0008734 0.00084826 0.00084135 0.00083513 0.00083398 0.00082068
0.00080583 0.00078979 0.00078059 0.00077476 0.00075448 0.00074426
0.00074421 0.00070195 0.00068741 0.00068138 0.00067262 0.00067125
0.00067033 0.00066035 0.00064729 0.00064205 0.00061964 0.00061794
0.00060835 0.00060465 0.00059548 0.00059479 0.00059461 0.00059436
0.00059426 0.00059411 0.00059406 0.00059392 0.00059365 0.00059351
0.00059191 0.00058798 0.00058682 0.00058148]]
[[ 1. 1. 18. 32. 62. 60. 63. 67. 61. 49. 31. 84. 50. 54.
15. 44. 44. 49. 31. 56. 88. 28. 88. 52. 17. 32. 38. 75.
3. 33. 48. 59. 35. 57. 47. 51. 19. 27. 72. 4. 84. 6.
55. 20. 58. 65. 61. 82. 42. 34. 40. 21. 43. 64. 39. 62.
36. 22. 79. 46. 16. 40. 41. 77. 16. 48. 78. 77. 89. 86.
27. 8. 87. 5. 25. 70. 80. 76. 75. 67. 65. 37. 2. 9.
73. 63. 29. 30. 69. 66. 68. 26. 71. 12. 45. 83. 13. 85.
74. 23.]]
[ 100.]
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[[ 0.01044297 0.0098214 0.00942165 0.00846471 0.00613666 0.00398615
0.00357754 0.0030054 0.00255861 0.00236574 0.00232631 0.00220291
0.00185227 0.0016354 0.0015979 0.00145072 0.00143661 0.00141369
0.00122685 0.00118978 0.00108457 0.00104251 0.00099215 0.00096401
0.0008708 0.00084773 0.00080484 0.00078507 0.00078378 0.00076876
0.00072774 0.00071732 0.00071348 0.00070812 0.00069253 0.0006762
0.00067269 0.00059905 0.00059367 0.000588 0.00056114 0.0005504
0.00051472 0.00051057 0.00050973 0.00048486 0.00047297 0.00046204
0.00044787 0.00043259 0.00042987 0.00042673 0.00041978 0.00040494
0.00040087 0.00039576 0.00039059 0.00037274 0.00036831 0.00036417
0.00036119 0.00034645 0.00034479 0.00034078 0.00033771 0.00033605
0.0003333 0.0003304 0.0003294 0.00032326 0.00031787 0.00031773
0.00031748 0.00031741 0.00031732 0.00031729 0.00031724 0.00031722
0.00031717 0.00031708 0.00031702 0.00031579 0.00030416 0.00030222
0.00029739 0.00029726 0.00028289 0.0002653 0.00026325 0.00024584
0.00024221 0.00024156 0.00023911 0.00023335 0.00021619 0.0002001
0.00019127 0.00018342 0.00017273 0.00015509]]
[[ 38. 1. 1. 16. 25. 38. 64. 24. 49. 56. 20. 3. 28. 2.
48. 19. 21. 62. 50. 6. 8. 7. 67. 18. 35. 53. 39. 55.
15. 57. 72. 52. 10. 5. 42. 43. 76. 22. 82. 4. 61. 23.
17. 16. 87. 62. 51. 60. 36. 58. 59. 33. 31. 54. 70. 11.
40. 79. 31. 9. 41. 77. 80. 34. 90. 89. 73. 13. 84. 32.
63. 29. 30. 69. 66. 68. 26. 71. 12. 45. 83. 14. 44. 78.
85. 46. 47. 19. 65. 74. 37. 27. 63. 88. 28. 81. 86. 75.
27. 18.]]
[ 100.]
EDIT: As per suggested answers, it is working when we use faster_rcnn_resnet101_coco_2017_11_08 model. But it is more accurate and that's why slower. I want this application with high speed because I'm going to use it in real time (on webcam) object detection. So I need to use faster model (ssd_mobilenet_v1_coco_2017_11_08)
解决方案
The problem is from the model: 'ssd_mobilenet_v1_coco_2017_11_08'
Solution: change to an differrent version 'ssd_mobilenet_v1_coco_11_06_2017' (this model type is the fastest one, change to other model types will make it slower and not the thing that you want)
Just change 1 line of code:
# What model to download.
MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'
When I use your code, nothing is shown but when I replace it with my previous experiment model 'ssd_mobilenet_v1_coco_11_06_2017' it works fine
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