bearpaw / pyranet Goto Github PK

local ResidualUp = n >= 2 and ResidualPyramid or Residual

as i am not familar with lua, what does this mean ? if n >=2 use res_pyra and others residual? but the parameters of the two function is not the same

When testing the six pyramids, you need to first change the 64 * 64 size of the heat map into 256 * 256, and then change it into the original size, such as 720 * 1280. If so, why do you need to go through 256 * 256, and in the process of transformation, does the center need to change according to the same scaling factor?

Thank you for sharing your wonderful work!

Is the model in Google Drive the same one you used to get 1st on MPII ? If that's so, that means you finally choose a 8-stack structure?

Have you done some experiments about number of stack based on your RPM ? For example, is there a big gap on performance between 2-stack and 8-stack?

Looking forward to your response!

the official link of LSP dataset is broken, can anyone share the LSP dataset. Thanks a lot

Hi,

Thanks for sharing your code!
I remember in the original hourglass code there are
`-- Small adjustment so cropping is less likely to take feet out

c[2] = c[2] + 15 * s
s = s * 1.25

`
before the crop operation.
However, I can't find the corresponding code in your project. It seems you only multiply a scale factor in the multi-scale testing. In the training and single scale testing, you only use the provided scale and center without any adjustment. Do I understand right? Do you think it's unnecessary for the above adjustment?

Thanks

Thank you for sharing your wonderful work!
Can I just use 'LEEDS_annotations.json' to train a model for LSP?
Thank you!

@bearpaw What about the frame rate and the hardware specification of this implementation ?

Hi, Thanks for your code!
The 'scale' and 'objpos' is also not provided offictially. How do you generate them?
Expect for your guidance! Thank you!

I have a SCI overhaul needs the result of [email protected]. Thank you very much

I want to train the model from a different dataset, then I find a problem, how can I get the hdf5 annotation data by myself? Could the author publish the code about how to transform the label data in the original dataset to the hdf5 formation?

May I ask that the prediction of single person attitude estimation is, the first step command is not quite right, how to operate

annolist_test = annolist(RELEASE.img_train == 0);

When I type an original command，it occurs an error: Undefined function or variable 'Imgidx' . How to solve ig
How should the command on lines 2-4 proceed?Where should I find the assignment of three variables?

How is the realtime perfommance? It's said that this model needs 45.9GFLOPs per image. Does it works on videos?

Hi, bearpaw
I am using your code to train my own dataset. Is it possible to fine tune on your model? My dataset has different keypoint from yours. Thanks.

Hi,

Thanks so much for your sharing the codes.

How can I use a trained model to make predictions on the MPII testing set and generate the .MAT file that can be evaluated by the mpii website http://human-pose.mpi-inf.mpg.de? Have you implemented such functions in your codes?

Hi, Thanks for your code!
But I just found original LSP dataset with 2000 images in the official website.
I am very glad for your guidance!

Sorry for multiple open issue. I just hesitate how to express my doubts and I am newcomer to github.
I am not skilled at pytorch. Therefore, I am not sure if your code uses 'LEEDS_annotations.json' directly to train a model on LSP dataset.
Thank you very much!

First, I really appreciate your work！
I want to write prms in pytorch.But the problem is ,loss and acc (badly)basically unchanged in the test set.Where did I go wrong?
thanks

Hi!

Thank you for publishing your work!

I was looking through the code for a way to visualize detections as skeletal coordinates. I was indeed able to find a drawSkeleton() function, which, in itself is being called by drawOuptput() and drawFeature(). Yet, in all calls to drawOutput() the "coords" argument is omitted, while there are no calls to drawFeature() at all. The following condition (in drawOutput()) is thus never satisfied:

if coords then
    im = drawSkeleton(input, coords, hms)
end

Am I missing something?

Thanks in advance!

Hi,@bearpaw
Thank you for sharing your wonderful work!
Based on the steps you provided, after training on the LSP data set, generate a .h5 prediction file, but in this evaluation code: http://human-pose.mpi-inf.mpg.de/results/lsp/evalLSP. Zip, it needs to use .mat files to make predictions. So what I want to ask is how to generate such a .mat file ? Have you implemented such functions in your codes?

Thanks for your great work!
Wonder that whether you would update it to PyTorch in your short-term plan? Thanks.

Hi,
I haven't found the parameter initialization described in your paper in this implementation.