NME metric about 3ddfa_v2 HOT 7 OPEN

For the NME part, you can refer to the benchmark of 3DDFA, here.
For PIFA, you may refer to the public code in their project page.

from 3ddfa_v2.

Can you share the expected NME of the three onnx models (mobilenet, mobilenet0.5, resnet22) on both datasets?
I've implemented it myself but I'm not sure I got the correct numbers.

from 3ddfa_v2.

@shachargluska could you share your code.

from 3ddfa_v2.

I hacked pieces from this repo to the benchmark of 3DDFA
Unfortunately I didn't keep it.
I do have the results:

mobilenet_v1_1.0_120x120 - 3.68%
mobilenet_v1_0.5_120x120 - 3.80%
resnet_22_120x120 - 3.67%

Those are nme over aflw2k3d

from 3ddfa_v2.

I hacked pieces from this repo to the benchmark of 3DDFA Unfortunately I didn't keep it. I do have the results:

mobilenet_v1_1.0_120x120 - 3.68% mobilenet_v1_0.5_120x120 - 3.80% resnet_22_120x120 - 3.67%

Those are nme over aflw2k3d

Could you share the code ? I will appreciate it very much, Sir!

from 3ddfa_v2.

I hacked pieces from this repo to the benchmark of 3DDFA Unfortunately I didn't keep it. I do have the results:
mobilenet_v1_1.0_120x120 - 3.68% mobilenet_v1_0.5_120x120 - 3.80% resnet_22_120x120 - 3.67%
Those are nme over aflw2k3d

Could you share the code ? I will appreciate it very much, Sir!

@lewisandJiang
Sorry, but I didn't save this work.

from 3ddfa_v2.

I hacked pieces from this repo to the benchmark of 3DDFA Unfortunately I didn't keep it. I do have the results:

mobilenet_v1_1.0_120x120 - 3.68% mobilenet_v1_0.5_120x120 - 3.80% resnet_22_120x120 - 3.67%

Those are nme over aflw2k3d
@shachargluska

I have applied benchmark.py3DDFA to 3DDFA_v2 by changing the load model codes, but I got weird results: mobilenet_v1_1.0_120x120 23.654% (aflw20003d) and 22.853%(aflw), should I change the "reconstruct_vertex" code or the code to calculate nme? Hoping for your reply, I will appreciate it very much, Sir!

from 3ddfa_v2.