Hey, I see that there are no tutorial notebooks for implementing machine learning algorithms and neural networks in PyTorch in this repo yet. PyTorch is gaining a lot of traction lately and is really going to be one of the most popular frameworks due to its dynamic computational graph & eager execution.

I would like to add such tutorial notebooks in PyTorch.

I am having difficulty in running this package as a Webservice. Would appreciate if we could provide any kind of documentation on implementing an API to get the keypoints from an image. Our aim is to able to deploy this API as an Azure Function and also know if it is feasible.

Platform :Win10
Python version:3.5
Source framework with version (like Tensorflow 1.4.1 with GPU):Caffe 1 cpu_oply
Destination framework with version (like CNTK 2.3 with GPU): Onnx cpu_only

In caffe framework, default add padding in average pooling layer.
You can find this in https://github.com/BVLC/caffe/blob/master/src/caffe/layers/pooling_layer.cpp.
While mmdnn seems not set count_inclu

It would be nice if you could automatically use the test set (test.txt if it exists) if no file is provided in the "Test a list of images" section of a trained model. The following code would do this.

--- a/digits/model/images/classification/views.py
+++ b/digits/model/images/classification/views.py
@@ -413,7 +413,10 @@ def classify_many():

     image_list = flask.request.files.get('ima

I have an implementation of recoverPose and would be happy to submit a PR if people found it useful.

Signature:

func RecoverPose(essentialMat Mat, points1 Mat, points2 Mat, R *Mat, t *Mat, focalLen float64, principalPoint image.Point, mask Mat)

Usage:

gocv.Reco

I am try to implement the same inference process as Faster-RCNN for R-FCN methods. Currently, I'm not able to know what the sample-faster-rcnn roi fusing layer which is the main plugin of whole process doing.
Does there any doc to tutor that? Or I just want to know how to get the middle inference output so that we can write the logic in CPU, and concat it with GPU stream.

Feature motivation

If I want to create a new chart in an experiment group, I have to individually select all experiments that I want to see. Very often I want to see all the experiments, and if I have more than a handful of experiments this becomes a bit painful.

Feature description

Add a Add all button to add all experiments to the chart in one click.

I download Caffe and Interactive zip ..
But i can't run them?

i use mac os please guide step by step

Thanks

We should have a table that calls our recommended batch_size per network per HW config (for the most common HW configs).
at the minimum we should call out the right configs for the AWS instances.

I am Android engineer,just follow tutorial "https://docs.opencv.org/3.4.0/d0/d6c/tutorial_dnn_android.html" linked there to find "MobileNetSSD_deploy.caffemodel" file but there is another one ; when i use the file "MobileNet_deploy.caffemodel"
the output is not work as expected

Observed Behaviour

One can get a shareable link if the canvas is empty.

Expected Behaviour

The share button should be disbaled

Would you like to work on it?

Yes

@Ram81