Howdy, Stranger!

It looks like you're new here. If you want to get involved, click one of these buttons!

Sign In

Howdy, Stranger!

It looks like you're new here. If you want to get involved, click one of these buttons!

Alert: Beginning Tuesday, June 25th, we will be freezing this site and migrating the content and forums to our new home at Check it out now!

There are errors loading custom TinyYolo graph

Hey guys,

I am a bit stuck here, let's load something simple, my custom caffe model has 2 categories (let's say dog and cat), and I need TinyYolo to load dog so that the second custom net would give me the breed of the dog, same goes with cat. (this is just an guide I am trying to write, please don't tell me the tiny_yolo already have cats and dogs, what if I add marine mammals as 3rd option) . So first I changed line 19 and 20 on

After that I am trying to change

First thing I did is changing line 207 and 212, where the array only have 2 items now, ['cat','dog'], [1,1]

But line 228 keeps giving me error
classification_probabilities = \
np.reshape(inference_result[0:980], (grid_size, grid_size, num_classifications))

ValueError: cannot reshape array of size 2 into shape (7,7,2)

Whole code that's causing issue is here, I tried to change grid_size, boxes_per_grid_cell, but none of the combination works, I am not really sure how this part of the code work and how can I load my own custom model?

    num_classifications = len(network_classifications) # should be 20
    grid_size = 7 # the image is a 7x7 grid.  Each box in the grid is 64x64 pixels
    boxes_per_grid_cell = 2 # the number of boxes returned for each grid cell

    # grid_size is 7 (grid is 7x7)
    # num classifications is 20
    # boxes per grid cell is 2
    all_probabilities = np.zeros((grid_size, grid_size, boxes_per_grid_cell, num_classifications))

    # classification_probabilities  contains a probability for each classification for
    # each 64x64 pixel square of the grid.  The source image contains
    # 7x7 of these 64x64 pixel squares and there are 20 possible classifications
    classification_probabilities = \
        np.reshape(inference_result[0:980], (grid_size, grid_size, num_classifications))


  • 4 Comments sorted by Votes Date Added
  • @Nyceane,
    The issue seems to be related to the mismatch in the shape of the 2 parameters you are passing to Numpy.reshape. The second parameter is hard coded to be a 3D array (7x7x2 in your case), but looks like the first parameter (inference_result[0:980]) is passing only a 2D array. The value of inference_result is defined by do_inference, so make sure that output is a 3D array. See:

    return self._filter_objects(output.astype(np.float32), input_image_width, input_image_height)

    Tagging @neal_at_intel, the author of this code.

  • @Nyceane For inference, Tiny Yolo returns an array of floats that can be interpreted as follows:

    • The first 980 float values correspond to each 7x7 grid cell and each of the 20 classes in the data set.
    • The next 98 float values correspond to the confidence values of each of the 2 bounding boxes anchored within each grid.
    • The remaining values are the bounding box coordinates (x,y,w,h) for each of the 2 bounding boxes in the grid cells.

    Make sure that your app includes a threshold for filtering out low confidence objects and duplicate bounding boxes. You can use the Tiny Yolo appzoo projects as reference. Thanks.

  • Hey Ashwin,

    [ 0.5 0.49975586]
    [ 0.5 0.49975586]

Sign In or Register to comment.