Skip to content
#

pruning

Here are 243 public repositories matching this topic...

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

  • Updated Jun 16, 2021
  • Python
sparseml
quic-ssiddego
quic-ssiddego commented Jul 16, 2021
  • Deprecate the usage of old quantsim implementation
  • Update bias correction implementation to use new quantsim api

If you are interested in working on this issue - please indicate via a comment on this issue. It should be possible for us to pair you up with an existing contributor to help you get started.

From a complexity perspective, this ticket is at an easy level.

Reference ImageNet implementation of SelecSLS CNN architecture proposed in the SIGGRAPH 2020 paper "XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera". The repository also includes code for pruning the model based on implicit sparsity emerging from adaptive gradient descent methods, as detailed in the CVPR 2019 paper "On implicit filter level sparsity in Convolutional Neural Networks".

  • Updated Jul 23, 2020
  • Python

Improve this page

Add a description, image, and links to the pruning topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the pruning topic, visit your repo's landing page and select "manage topics."

Learn more