I wonder do you have plans to support resuming from checkpoints for classification? As we all know, in terms of training ImageNet, the training process is really long and it can be interrupted somehow, but I haven't notice any code related to "resume" in scripts/classification/train_imagenet.py.
I'm a little bit confused when I run and analysis your repo
for i in range(17):
for s in subset:
index = s[np.array(util.limbSeq[i]) - 1]
if -1 in index:
continue
cur_canvas = canvas.copy()
how to explain and understand s[np.array(util.limbSeq[i]) - 1] ? can we use util.limbSeq[i]) - 1 to replace the s[np.array(util.limbSeq[i]) -
@Lotayou (a.k.a myself) I just found that my SMPL UV data at hand is messed up, UV vertices' topology does not match that of SMPL 3D mesh. Here's the result:
libfaceid is a research framework for prototyping of face recognition solutions. It seamlessly integrates multiple detection, recognition and liveness models w/ speech synthesis and speech recognition.
Hi, thanks for the great code!
I wonder do you have plans to support resuming from checkpoints for classification? As we all know, in terms of training ImageNet, the training process is really long and it can be interrupted somehow, but I haven't notice any code related to "resume" in
scripts/classification/train_imagenet.py.Maybe @hetong007 ? Thanks in advance.