Video showing but no object detections about ncnn-android-yolov7 HOT 9 OPEN

不，我自己也无法解决，因为我既不了解 java 也不了解 C，而那t是那里使用的 2 种语言

You can try changing in the yolo.cpp file ex.extract("out0", out); out0 out1 out2. When I make changes based on three output name in the .param file, it can be work. also the input name in0
But another problem arises, the detection results were terrible

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I have the same problem ，I get video feed but no detections

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Have you solved it？I have the same problem

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No, and I won't be able to solve it myself because I know neither java nor C, and those are the 2 languages used there

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Hi everybody! And big thanks to Xiang-Wuu for the job!!
I have the same problem on android device, with coco it's running fine on phone but when we put our weights and model (.bin and .param) and modify number of classes (nc) and names class, build run correctly but no boundingboxes on screen.
I see a china issue ( https://blog.csdn.net/qq_43268106/article/details/127139216 ) where talking about .param where to delete the first ten layers and modificate the first layer in0 to image but i have no ideas how to do that...

He talk about "generate_proposals" but i don't understand what it's mean!

Thanks to yours feedbacks ;-)

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Has anyone managed to resolve or at least find out what the problem is? Apparently, the solution only works with yolo's own weights, it doesn't work with custom weights. This greatly limits its use.

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不，我自己也无法解决，因为我既不了解 java 也不了解 C，而那t是那里使用的 2 种语言

You can try changing in the yolo.cpp file ex.extract("out0", out); out0 out1 out2. When I make changes based on three output name in the .param file, it can be work. also the input name in0 But another problem arises, the detection results were terrible

Can you explain your suggestion better?
apparently out0, out1, out seem to be correct and compatible with the parameter file.
What would be the necessary change?

{
ncnn::Mat out;
ex.extract("out0", out);
ncnn::Mat anchors(6);
anchors[0] = 12.f;
anchors[1] = 16.f;
anchors[2] = 19.f;
anchors[3] = 36.f;
anchors[4] = 40.f;
anchors[5] = 28.f;

    std::vector<Object> objects8;
    generate_proposals(anchors, 8, in_pad, out, prob_threshold, objects8);

    proposals.insert(proposals.end(), objects8.begin(), objects8.end());
}

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Install netron first，open the Model file（.param） with netron（https://netron.app/）, find the three Convolution layer above the three Permute layers, click to view details, you can see its name ( the location indicated by the red arrow), fill in the three names with the corresponding out0, out1, out2, and you can run the detection.

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