Comments (2)
@alter-sachin
This example works specifically with mobilenet and yolo2 network architectures. Your image segmentation model has a different architecture (I'm guessing it's U-NET given that you use fast.ai). That means your model will produce output different from such of yolo2, which means you'll need to change all the code related to processing the output. The way that image is fed into the network can differ slightly as well.
I'm sure you understand that the purpose of this repository isn't to teach deep learning. The direction that I would go if I were you is to find a clear python example of inference using your model that works and try to port python code to C#. This can be a time-consuming and tedious process, but that is the process I followed to create this example for yolo2.
Wish you good luck.
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Thanks :)
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Related Issues (13)
- App crashes after few seconds. HOT 8
- tensorflow model to Barracuda or Onnx HOT 2
- TF2 (Keras) models in Unity
- Combining object detection with AR HOT 1
- Working on a YOLO-Barracuda implementation
- barracuda not success to convert the pb file HOT 2
- Yolo Output grid (1,13,13,30) instead of (-1,-1,-1,-1) HOT 3
- Input shape Mismatch when converting another keras model to onnx HOT 4
- Using this project with AR glasses HOT 1
- Not working on Android HOT 1
- Covert a cyclegan model to barracuda HOT 1
- I
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