Slimmable Networks

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

0. Requirements:
   • python3, pytorch 1.0, torchvision 0.2.1, pyyaml 3.13.
   • Prepare ImageNet-1k data following pytorch example.
1. 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.
2. 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:

• Switchable batchnorm and slimmable layers are implemented in models/slimmable_ops.
• Slimmable training is implemented in these lines in train.py.

License

CC 4.0 Attribution-NonCommercial International

The software is for educaitonal and academic research purpose only.