Skip to content
master
Go to file
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 
 
 

README.md

Slimmable Networks

version pytorch license

An open source framework for slimmable training on tasks of ImageNet classification and COCO detection, which has enabled numerous projects. 1, 2, 3

1. Slimmable Neural Networks ICLR 2019 Paper | OpenReview | Detection | Model Zoo

Illustration of slimmable neural networks. The same model can run at different widths (number of active channels), permitting instant and adaptive accuracy-efficiency trade-offs.

2. Universally Slimmable Networks and Improved Training Techniques ICCV 2019 Paper | Model Zoo

Illustration of universally slimmable networks. The same model can run at arbitrary widths.

3. AutoSlim: Towards One-Shot Architecture Search for Channel Numbers NeurIPS 2019 Workshop Paper | Model Zoo

AutoSlimming MobileNet v1, MobileNet v2, MNasNet and ResNet-50: the optimized number of channels under each computational budget (FLOPs).

Run

  1. Requirements:
    • python3, pytorch 1.0, torchvision 0.2.1, pyyaml 3.13.
    • Prepare ImageNet-1k data following pytorch example.
  2. Training and Testing:
    • The codebase is a general ImageNet training framework using yaml config under apps dir, based on PyTorch.
    • To test, download pretrained models to logs dir and directly run command.
    • To train, comment test_only and pretrained in config file. You will need to manage visible gpus by yourself.
    • Command: python train.py app:{apps/***.yml}. {apps/***.yml} is config file. Do not miss app: prefix.
    • Training and testing of MSCOCO benchmarks are released under branch detection.
  3. Still have questions?
    • If you still have questions, please search closed issues first. If the problem is not solved, please open a new.

Slimmable Model Zoo

Slimmable Neural Networks

Model Switches (Widths) Top-1 Err. FLOPs Model ID
S-MobileNet v1 1.00
0.75
0.50
0.25
28.5
30.5
35.2
46.9
569M
325M
150M
41M
a6285db
S-MobileNet v2 1.00
0.75
0.50
0.35
29.5
31.1
35.6
40.3
301M
209M
97M
59M
0593ffd
S-ShuffleNet 2.00
1.00
0.50
28.6
34.5
42.8
524M
138M
38M
1427f66
S-ResNet-50 1.00
0.75
0.50
0.25
24.0
25.1
27.9
35.0
4.1G
2.3G
1.1G
278M
3fca9cc

Universally Slimmable Networks and Improved Training Techniques

Model Model ID Spectrum
US‑MobileNet v1 13d5af2 Width
MFLOPs
Top-1 Err.
1.0
568 
28.2 
0.975 
543 
28.3 
0.95 
517 
28.4 
0.925 
490 
28.7 
0.9 
466 
28.7 
0.875 
443 
29.1 
0.85 
421 
29.4 
0.825 
389 
29.7 
0.8 
366 
30.2 
0.775 
345 
30.3 
0.75 
325 
30.5 
0.725 
306 
30.9 
0.7 
287 
31.2 
0.675 
267 
31.7 
0.65 
249 
32.2 
0.625 
232 
32.5 
0.6 
217 
33.2 
0.575 
201 
33.7 
0.55 
177 
34.4 
0.525 
162 
35.0 
0.5 
149 
35.8 
0.475 
136 
36.5 
0.45 
124 
37.3 
0.425 
114 
38.1 
0.4 
100 
39.0 
0.375 
89 
40.0 
0.35 
80 
41.0 
0.325 
71 
41.9 
0.3 
64 
42.7 
0.275 
48 
44.2 
0.25
41
44.3
US‑MobileNet v2 3880cad Width
MFLOPs
Top-1 Err.
1.0 
300 
28.5 
0.975 
299 
28.5 
0.95 
284 
28.8 
0.925 
274 
28.9 
0.9 
269 
29.1 
0.875 
268 
29.1 
0.85 
254 
29.4 
0.825 
235 
29.9 
0.8 
222 
30.0 
0.775 
213 
30.2 
0.75 
209 
30.4 
0.725 
185 
30.7 
0.7 
173 
31.1 
0.675 
165 
31.4 
0.65 
161 
31.7 
0.625 
161 
31.7 
0.6 
151 
32.4 
0.575 
150 
32.4 
0.55 
106 
34.4 
0.525 
100 
34.6 
0.5 
97 
34.9 
0.475 
96 
35.1 
0.45 
88 
35.8 
0.425 
88 
35.8 
0.4 
80 
36.6 
0.375 
80 
36.7 
0.35
59
37.7 

AutoSlim: Towards One-Shot Architecture Search for Channel Numbers

Model Top-1 Err. FLOPs Model ID
AutoSlim-MobileNet v1 27.0
28.5
32.1
572M
325M
150M
9b0b1ab
AutoSlim-MobileNet v2 24.6
25.8
27.0
505M
305M
207M
a24f1f2
AutoSlim-MNasNet 24.6
25.4
26.8
532M
315M
217M
31477c9
AutoSlim-ResNet-50 24.0
24.4
26.0
27.8
3.0G
2.0G
1.0G
570M
f95f419

Technical Details

Implementing slimmable networks and slimmable training is straightforward:

License

CC 4.0 Attribution-NonCommercial International

The software is for educaitonal and academic research purpose only.

About

Slimmable Networks, AutoSlim, and Beyond, ICLR 2019, and ICCV 2019

Topics

Resources

License

Releases

No releases published

Packages

No packages published

Languages