🔥CNN Structured Pruning Leaderboard

Welcome to our dedicated site for the survey paper: "Structured Pruning for Deep Convolutional Neural Networks: A Survey". Our survey is accepted by IEEE T-PAMI. Links include arXiv and IEEE Xplore.

Github Repo: Awesome Pruning: A curated list of neural network pruning resources.

This platform serves as a repository and visual representation of the benchmarks from studies covered in our survey.

Here, you can explore the reported accuracy and FLOPs metrics from various papers, providing an at-a-glance view of the advancements and methodologies in the domain of structured pruning.

If you find this website helpful, please consider citing our paper 😊

Search by below options:

Section
Year
Method
Model
Acc
Acc Pruned
Acc ↓ (%)
FLOPs (M)
FLOPs Pruned (M)
FLOPs ↓ (%)
Params (M)
Params Pruned (M)
Params ↓ (%)
2.6.1
2022
AutoCompress
PreResNet-101
94.81
94.85
-0.04
253.15
1232.04
19.07
44.54
31.18
20.19
Draw with

Draw with [model, section, year]

Set x-axis

Set x-axis to [FLOPs after pruning, FLOPs drop (%)]

Section
Year
Method
Model
Acc
Acc Pruned
Acc ↓ (%)
FLOPs (M)
FLOPs Pruned (M)
FLOPs ↓ (%)
Params (M)
Params Pruned (M)
Params ↓ (%)
Dataset
2.6.1
2022
AutoCompress
PreResNet-101
94.81
94.85
-0.04
253.15
1232.04
19.07
44.54
31.18
20.19
cifar10

Click any Method Name in above table to see details...