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Predictions of custom converted YOLOv4 model #443
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@JonathanSamelson do you get the same error with the onnx conversion of standard yolov4 model? |
@jingyanwangms Thanks for your answer. No, it works well with the model available at |
@JonathanSamelson Yes, you're right. That postprocess function works only if the model outputs have the shape |
@ravimashru Thanks. Do you see any reason why I have a shape that is so different even though I have the same model input size (416)? Could it be related to the number of classes (11 in my case)? Or is it related to the conversion...? |
Honestly, your guess is as good as mine. Is it possible for you to share the code you wrote to create your model in TensorFlow? I can try looking at that to help you figure it out. |
I did not create the model in Tensorflow but instead converted the .weights file after training on Darknet. Here is the notebook I used for conversion. |
Is there any updates ? |
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Hi. I'm new to inference using ONNX and I'm getting troubles to use the predictions I get using my custom YOLOv4 model, recently converted thanks to this notebook.
Then, I wanted to use the notebook for inference, but I'm not getting the same kind of output. Instead of something like
[(1, 52, 52, 3, 85), (1, 26, 26, 3, 85), (1, 13, 13, 3, 85)]
, I get[(1, 46, 15)]
.Next, I get the following error using the
postprocess_bbox
function:Of course, it does not have the same shape... But I don't understand the meaning of the shape I obtain, knowing that my model output 11 classes and the input resolution is 416, just like the example. In case it helps, my output_names is :
[tf.concat_16]
In my custom model, I did not change the anchors, the strides nor the xy scales, it was simply trained with the default values.
Does anyone know the meaning of the shape I get or what's wrong with my model to obtain such a shape?
Is this issue related to a specific model?
Model name (e.g. mnist): YOLOv4 (custom)
Model opset (e.g. 7): 11 (default in the notebook)
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