Object Detection API (issue)

I am using this colab file to run mask rcnn object detection model. Colab تشخیص شیء TensorFlow Hub

For visualization purposes it uses TensorFlow Object Detection API.

print('loading model...')
hub_model = hub.load(model_handle)
print('model loaded!')

# running inference
results = hub_model(image_np)

# different object detection models have additional results
# all of them are explained in the documentation
result = {key:value.numpy() for key,value in results.items()}

I don’t want to rely on object detection API. But I want to use models from thub. Now, how can I get instance wise segmentaiton mask for input image?

The results key above doesn’t have it. I’m expected to get instance mask as the following format.


So, if a image contains 1 people, 2 dog, 1 cant, the shape would be


where, assume that,

1, HEIGHT, WITHD, 1 - for 1st people
1, HEIGHT, WITHD, 1 - for 2nd people
1, HEIGHT, WITHD, 1 - for 3rd people
1, HEIGHT, WITHD, 1 - for 4th people

But the results don’t give such formatted output.

result = {key:value.numpy() for key,value in results.items()}

for using that Mask RCNN, I’d suggest reading their visualisation method to have a better understanding on how they do the segmentation.

for something closer to what you are expecting, you might use a model for image segmentation like:

as far as I remember they have an output similar to what you are expecting

@lgusm Thanks for the suggestion. I’ve already visited those scirpts.

I am not using semantic segmentation and I need to use instance segmentation. The output of thub_mask_rccn model does give detection_mask output but it only gives cropped and resize version of intance mask. But I am expectin get location aware each mask channel.