am15h
YoloV3 conversion for the current model
Hello there,
I have been trying to implement this code for my custom model which uses YoloV3. When I get to the _outputShapes part, I get an error since the default model and mine aren't compatible.
The default model's output shape looks like this: [[1, 10, 4], [1, 10], [1, 10], [1]]
My model's output shape on the other hand, looks like this: [[1, 10647, 6]]
Here is where the problem is. I need to convert this part of code for my model.
TensorBuffer outputLocations = TensorBufferFloat(_outputShapes[0]);
TensorBuffer outputClasses = TensorBufferFloat(_outputShapes[1]);
TensorBuffer outputScores = TensorBufferFloat(_outputShapes[2]);
TensorBuffer numLocations = TensorBufferFloat(_outputShapes[3]);
What do I need to do in order for my custom model to work?
@Abdullah-Majeed @ayazmhmd Yep.
To put it simply, a step is missing to convert the current ouput ( [[1, LARGE_NUMBER, SMALL_NUMBER]] ) to the default one. The missing step is the non max suppression (NMS). I successfully converted the output by reimplementing this algorithm in Flutter (to check the algo, you can check the official YOLOv5). I can suggest to you to use the package matrix2d to help you with the reimplementation.
