Good Morning i would like to have an understanding of this output i have when i run my model using the blob file. I saved the onnx file from a TensorFlow model and specified the shape as such [1, 15, 224, 224, 3]. By using this shape i was able to convert the onnx into blob file. I begun to work with this file and tried to implemented to run the inference. My task is video classification, with just 2 categories on the whole frame so not object detection or face/person detection. When i run the python code i got the following output looping and referring to the size of my nn being no the right shape. However it seems odd cause it give me the shape as [3, 224] and it seems that is taking the 3 which 9is referred to RGB channel as one shape. what is a possible solution? I would like to point out that this screenshot refers to shape [300,300], even by changing this to [224,224] will output the same error.

Over this section it talks about the shape arrangement. however how would i rearrange this? Does this apply also to my custom model?

    jakaskerl i Will try wit the layout option and I will also try the other opinion using the API directly like mentioned in this piece of code just after.

    import depthai as dai
    pipeline = dai.Pipeline()
    colorCam = pipeline.createColorCamera()
    colorCam.setInterleaved(False) # False = Planar

    I will let you know which one solve the problem

        nikocreatina
        The interleaved must be false anyway I think, otherwise you get weird results. Also, it speeds up image manipulations.

            5 days later

            Hi There i still got the problem even after following those steps.

            jakaskerl

            Inputs

            Name: x, Type: DataType.U8F, Shape: [3, 224, 224, 15, 1] (StorageOrder.???)

            Outputs

            Name: output_node, Type: DataType.FP16, Shape: [2, 1] (StorageOrder.NC)

            I just double checked the input shape and it is as follow. My onnx file definetly have an input of 1,15,224,224,3. 15 is the number of frames and 1 is the batches. Any ideas?