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🚀 Real Time Pose Estimation in TensorLayer #434
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I release the APIs to download or visualise MPII dataset in 1 line of code here: #482 import pprint
import tensorlayer as tl
img_train_list, ann_train_list, img_test_list, ann_test_list = tl.files.load_mpii_pose_dataset()
print(img_train_list[0])
pprint.pprint(ann_train_list[0])
for i in range(100): # show 100 images
image = tl.vis.read_image(img_train_list[i])
tl.vis.draw_mpii_people_to_image(image, ann_train_list[i], '_temp%d.png' % i) |
Any ideas to make it run faster?Algorithm
Engineering
|
I think where you can achieve better results easier is in post-processing. At least in tf-pose-estimation that's the current bottleneck. maybe implementing a c++/cython multiprocessing module for that can be enough |
Implementing openpose in #765 |
I'm trying to implement a real time pose estimation(mulit-person) on mobile. For the mobile performence, I'm trying to quantize the model. |
@1icas I think mobilenet is a easier approach than quantisation.. |
@zsdonghao yes,i know. But it's not satisfy the performance requirement. Also i'm very interested in quantisation. |
Or do you have other suggesstion for model acceleration ? |
A discussion for real-time (multi-person) pose estimation using TensorLayer and TensorFlow
Paper List
Existing code/resource
"Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields"
provides some links in his github: ZheC/Realtime_Multi-Person_Pose_Estimation, [video], [training log]pip3 install pycocotools
, see keras_Realtime_Multi-Person_Pose_EstimationBlog
About Data Augmentation
Others
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