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GoogleNet Inception v3
I have tried to implement and use your version of inception v3, (thanks alot for doing this work) I have attached my prototxt for the implementation of inception v3.
inceptionv3_prototxt.txt
I am om nvidias caffe-0.15 branch.
Training goes fine and i can see the accuracy on the validation set aswell. My problem occurs when i try test the model on a single image.
When i do this i get the following error:
insert_splits.cpp:29] Unknown bottom blob 'bn1' (layer 'conv1/3x3_s1_scale', bottom index 0)
Any ideas how to fix this issue, so i can use the model to classify new images.
Thanks alot.
Best regards,
William
Hi,
Can you share your trained .caffemodel file?
Thanks
May you send your deploy.prototxt to me ? I want to test my model,but I encounter a problem with the file.
Is the BN not complete? Because the prototxt file doesn't contain ScaleLayer after BatchNormLayer,
The last layers should hold different names (or types) :
layer {
name: "loss1/top-1" <== the name is loss
type: "Accuracy" <== the type is accuracy
bottom: "loss1/classifier"
bottom: "label"
top: "loss1/top-1"
include {
phase: TEST
}
}
layer {
name: "loss1/top-3" <== the name is loss
type: "Accuracy" <== the type is accuracy
bottom: "loss1/classifier"
bottom: "label"
top: "loss1/top-3"
include {
phase: TEST
}
accuracy_param {
top_k: 3
}
It should be like this :
layer {
name: "acc/top-1"
type: "Accuracy"
bottom: "fc1"
bottom: "label"
top: "acc/top-1"
include {
phase: TEST
}
}
layer {
name: "acc/top-5"
type: "Accuracy"
bottom: "fc1"
bottom: "label"
top: "acc/top-5"
include {
phase: TEST
}
accuracy_param {
top_k: 5
}
}
I train your Inception_v3_GoogLeNet by caffe, it is successful. But I train Inception_v4 by caffe, it is wrong.
inception_v4_train_test.prototxt.txt
The mistake is
Check failed: top_shape[j] == bottom[i]->shape(j) (45 vs. 46) All input
s must have the same shape, except at concat_axis.
So,I want to konw how you modofy your Inception_v3_GoogLeNet through original Inception_v3.
thanks.
The paper specified using a 299x299 input, why do you use a 224x224 input, is there a performance difference compared to using 299x299?
However, I never get error like this when I train other nets like googLeNet. Here are logs:
F0925 14:07:56.489186 14091 math_functions.cu:79] Check failed: error == cudaSuccess (74 vs. 0) misaligned address
*** Check failure stack trace: ***
@ 0x7f93c3722daa (unknown)
@ 0x7f93c3722ce4 (unknown)
@ 0x7f93c37226e6 (unknown)
@ 0x7f93c3725687 (unknown)
@ 0x7f93c3ec36c8 caffe::caffe_gpu_memcpy()
@ 0x7f93c3e7e58e caffe::SyncedMemory::to_gpu()
@ 0x7f93c3e7da39 caffe::SyncedMemory::gpu_data()
@ 0x7f93c3d2e522 caffe::Blob<>::gpu_data()
@ 0x7f93c3effe3e caffe::BiasLayer<>::Backward_gpu()
@ 0x7f93c3f00917 caffe::ScaleLayer<>::Backward_gpu()
@ 0x7f93c3eaa097 caffe::Net<>::BackwardFromTo()
@ 0x7f93c3eaa201 caffe::Net<>::Backward()
@ 0x7f93c3e9df92 caffe::Solver<>::Step()
@ 0x7f93c3e9e849 caffe::Solver<>::Solve()
@ 0x40843e train()
@ 0x405c8c main
@ 0x7f93c227ff45 (unknown)
@ 0x40645d (unknown)
@ (nil) (unknown)
I have checked my address, but nothing wrong.
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