![]() The coordinates (relative to top left) are normalized to the width and height of the image. ![]() Hey presto! This will print out a list of dictionaries like Put everything in one directory then simply execute the code: python predict.py image.jpg Which will give you a zipfile containing: labels.txt You seem to be using python, so you can export the object-detection model from the customvision UI (select tensorflow options): Where am I going wrong? Any suggestions are welcome. ![]() ![]() If I am detecting just a single object, how do I get the bounding boxes from the model_outputs:0 tensor. The Placeholder:0 takes a tensor of shape (?,416,416,3) and the model_outputs:0 outputs a tensor of shape (1, 13, 13, 30). The Placeholder:0 and model_outputs:0 are the inputs and the outputs. Ops = tf.get_default_graph().get_operations() With tf.Session(graph=detection_graph) as sess: Graph_def.ParseFromString(serialized_graph) With tf.gfile.GFile('model.pb,'rb') as fid: This is the code that I am using the print out the operations for a tensorflow model and the output. Can we do the same for custom vision's model.pb file? Say for example, we can get the bounding boxes from the ssd frozen inference graph.pb file as there are tensors present. Is there a way to get bounding boxes of a particular object detected via Microsoft custom vision model.pb file? I know we can get that via API calls to the azure custom vision service.
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June 2023
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