Nothing is being detected in Tensorflow Object detection API

user_5353128 0
Viewed 6465

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.

enter image description here

another image without any detection.

enter image description here

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.        ]
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  [ 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.        ]
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  [ 0.          0.          1.          1.        ]
  [ 0.74907303  0.14624023  1.          1.        ]
  [ 0.          0.          1.          1.        ]
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  [ 0.          0.20880508  1.          1.        ]
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  [ 0.74907303  0.14624023  1.          1.        ]
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  [ 0.74907303  0.14624023  1.          1.        ]
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  [ 0.74907303  0.14624023  1.          1.        ]
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  [ 0.74907303  0.14624023  1.          1.        ]
  [ 0.74907303  0.14624023  1.          1.        ]
  [ 0.74907303  0.14624023  1.          1.        ]
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  [ 0.74907303  0.14624023  1.          1.        ]
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  [ 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.          0.          1.          1.        ]
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  [ 0.          0.          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.]
[[[ 0.          0.          1.          1.        ]
  [ 0.          0.          1.          1.        ]
  [ 0.          0.68494415  1.          1.        ]
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  [ 0.          0.68494415  1.          1.        ]
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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
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   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)

object-detection tensorflow python
5 Answers

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

user_43973490

As a workaround change #MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_08' to MODEL_NAME = 'faster_rcnn_resnet101_coco_2017_11_08'.

user_86042230

You can use older 'ssd_mobilenet_v1 ... ' and run your program completely with boxes (I run it just now and it is correct). This is a link to this older version. Hope they correct newer version soon!

user_82467100

I used to have the same problem.

But a new model has been upload it recently 'ssd_mobilenet_v1_coco_2017_11_17'

I tried it and works like charm :)

user_78625050

the function visualize_boxes_and_labels_on_image_array has the following code:

  for i in range(min(max_boxes_to_draw, boxes.shape[0])):
    if scores is None or scores[i] > min_score_thresh:

so, the score must be bigger than min_score_thresh (default 0.5), you can check whether there are some scores bigger than it.

user_89249210
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