Matterport | pretty awesome dataset for RGB-D machine learning tasks | Dataset library

I would like to save the loss data while training my Mask R CNN, but I seem to be missing something. The training is working but I'm getting the Error:

AttributeError: 'NoneType' object has no attribute 'history'

In the balloon.py file in Detectron2 samples, I get a KeyError of 'regions' whenever I run the balloon.py on my custom dataset. I figured something was wrong with the json file in the train folder, so I first used the latest VIA 3 and then VIA 2.0.0. Both jsons create the same KeyError.

I compared the balloon's training VIA json to my training VIA json, and they have the same structure now, so I'm thinking it isn't a json issue anymore. Why would Python not be able to read a string as a key?

Here's balloon.py: https://github.com/matterport/Mask_RCNN/blob/master/samples/balloon/balloon.py

Is it mandatory to annotate images using polygon shapes for mask rcnn? I read the https://github.com/matterport/Mask_RCNN and the research paper as well. It seems that matterport's implementation can take bounding box as well as polygon as annotations. Although I am not certain. So should I consider bounding box annotation for my dataset? or polygon annotation?

Currently I have annotated some images using bounding box on Intel's CVAT.

This is my current Flask code that works fine, it receives a POST request with the image from the client, runs it through the model (based on this GH: https://github.com/matterport/Mask_RCNN), and sends a masked image back to the client.

However, it is loading the model from the Configuration file and loading the weights for each request, which takes ages. I want to load the model on server startup and the weights and pass that to the index function. I have tried the solutions from other questions, but with no luck. I wonder if it's because I am loading a model, and then weights, rather than just loading a single h5 model file?

How do I load a file on initialization in a flask application Run code after flask application has started

Flask app:

There are 2 examples in the Matterport SDK for Embeds documentation to show how to place Mattertags in a scene:

• The Intersection Inspector which only allows you to see coordinates for placing a Mattertag where the cursor is if you wait a little bit ... Not very user friendly, you need to copy the coordinates manually in your program.
• The Transient Tags Editor which enable you to interactively place multiple Mattertags visually, edit them and then to extract them easily in a JSON file ...

I was wondering how to reproduce the Transient Tags Editor visual UX as I would like to use it in an application.

I am looking to use only one class, person (along with BG, background), for the Mask RCNN object detection. I am using this link: https://github.com/matterport/Mask_RCNN to run the mask rcnn. Is there a specific way to complete this (editing specific files, creating an extra python file, or just by filtering selections from the class_names array)? Any direction or solution will be highly appreciated. Thank you

I am a newbie to deep learning so while I am trying to build a Masked R-CNN model for training my Custom Dataset I am getting an error which reads:

I am using MaskR-CNN to do object segmentation in 2d-pictures. I use previously written code from someone else. My problem is, that I got problems with many false positives. Now I'd like to check the validation results after each epoch and check for false positives to optimize on that. Maybe not just on that but mixed with F1 score.

The code I use just calls the maskrcnn train method like that:

I wish to serialize an entire class to json. However, most of the variables I need are static variables (not defined within __init__()). Is there a Pythonic way to do that, other then the naive solution, i.e., calling all variables by name?

So far, I tried to call json.dump() with an encoder to handle numpy arrays: