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This repository was archived by the owner on Nov 28, 2022. It is now read-only.
This repository was archived by the owner on Nov 28, 2022. It is now read-only.

Confidence varies for each API call (for the same picture) #3

@v-fuchs

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@v-fuchs

Hi,

thank you again for providing the GRE solution which is very helpful for us. Unfortunately we still have a big problem.

We trained a caffemodel on a NVIDIA DevBox with 2 Titan X, nvcaffe 0.15, DIGITS 4.0, CUDA 8.0, cuDNN 5.1, GoogleNet for 20 classes. The trained model is working like a charm when classifying pictures with DIGITS itself and the predictions are correct AND ALWAYS have the same response (when repeating the inference on the same picture).

Unfortunately, when importing the caffemodel (snapshot.caffemodel, deploy.prototxt, labels.txt, mean.binaryproto) to the GRE solution (we just replaced your model with ours in the Dockerfile.inference_server and left everything else as it is) the predictions of the same image are always changing and aren't always correct.

We deployed our caffemodel to different devices (compiled caffe on iOS or Android, used caffe standalone, used several different versions, ...) and the predictions were always correct and had the same value for the same picture.

We really would like to use the GRE solution for our projects but we can't find the problem.

We really appreciate any kind of help!

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