[NeuralNetwork(0)] [warning] Input image (224x224) does not match NN (3x224)

@Nadi Why would you need input layer? If you want to overlay the semantic seg. result with the rgb image, you can use neuralNetworkNode.passthrough output to get the input image. Thoughts?

[184430107165680F00] [3.3] [89.777] [NeuralNetwork(0)] [warning] Input image (300x300) does not match NN (3x300)

Model: MobileNetV2

I am getting the same error and I tried the above two options which you have mentioned, but no luck.
I am assuming that it is just a warning, will it affect in any way.

heebahsaleem
It will not work.
You need to change:

• either the Openvino version you are using (trial and error), they seem to change their default shapes between versions.
• when compiling the blob with blobconverter, use these https://docs.openvino.ai/2022.3/openvino_inference_engine_tools_compile_tool_README.html parameters (primarily -il) to set the desired shape.

Thanks,
Jaka

I'm having a similiar issue

[18443010D18F411300] [2.2] [9.560] [DetectionNetwork(1)] [warning] Input image (244x244) does not match NN (3x244)

My blob conversion:
blob_path = blobconverter.from_tf(

    frozen_pb="./frozen_graph.pb",

    data_type="FP16",

    shaves=6,

    optimizer_params=[

        f"--input_shape=[1,{SHAPE},{SHAPE},3]"

    ]

)

I'm confused about how I should modify this blob_path to make sure it outputs the desired shape/right convention @jakaskerl

Hi heebahsaleem

1. Trial and error is referring to the process of finding the right Openvino version for your model to work. Basically you would have to compile the model with each Openvino version, untill you find the right one (perhaps 2021.4 could work right away), hence the "trial and error".
2. Inside blobconverter, you have "advanced" tab which allows you to select the number of shaves you are using, and pass in additional parameters. Under "Compile parameters:" you should be able to pass in the -il parameter and the shape you want.

userOfCamera
According to README I sent above the shapes should be defined as either "NCHW" or "NHWC". So the compiler knows how to reshape the input.

Thanks,
Jaka

@jakaskerl
I set --layout to both nchw and nhwc but the same issue persists

blob_path = blobconverter.from_tf(

    frozen_pb="./frozen_graph.pb",

    data_type="FP16",

    shaves=6,

    optimizer_params=[

        f"--input_shape=[1,{SHAPE},{SHAPE},3]",

        "--layout=nhwc"

    ]

)

Am I supposed to set this using the -il flag? Can you show me an example