Semantic_Segmentation | Semantic Segmentation using Fully Convolutional | Machine Learning library

It looks like your python code assumes the current working directory of the process is the D:/semantic_segmentation/scripts directory, which is not necessarily the case.

Here are two solutions:

• change the working directory of your c++ program with chdir or the windows equivalent, or
• change the python code to compute an absolute path starting from the script file instead of the current working directory. See this answer.

I believe you have reached out to us regarding the same query. I just wanted to paste the answer here for others to reference:

"The detection model usually have 4 output tensors to specifies the locations, classes, scores, and number and detections. You can read more about it here. In contrary, the segmentation model only have a single output tensor, so if you treat it the same way, you'll most likely segfault trying to access the wrong memory region. If you want to do all three tasks on the same image, my suggestion is to create 3 different engines and feed the image into each. The only problem with this is that each time you switch the model, there will likely be data transfer bottleneck for the model to get loaded onto the TPU. We have here an example on how you can run 2 models on a single TPU, you should be able to modify it to take 3 models."

On the last note, I just saw that you added:

how do i associate the labels.txt file that I made based off of the label info of the model to these colors

I just don't think this is something you can do for segmentation model but maybe I'm just confused on your query?

Take object detection model for example, there are 4 output tensors, the second tensor gives you an array of id associates with a certain class that you can map to a a label file. Segmentaion models only give the pixel surrounding an objects.

[EDIT] Apology, looks like I'm the one confused on segmentation models. Quote form my college :) "You are interested to know the name of the label, you can find the corresponding integer to that label from result array in Semantic_segmentation.py. Where result is classification data of each pixel.

For example;

if you print result array in the with bird.jpg as input you would find few pixel's value as 3 which is corresponding 4th label in pascal_voc_segmentation_labels.txt (as indexing starts at 0 )."