mindcv_fixed/configs/squeezenet

• SqueezeNet
• • Introduction
  • Benchmark
  • • • Notes
  • Examples
  • • Train
    • Eval

SqueezeNet

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SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

Introduction

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SqueezeNet is a smaller CNN architectures which is comprised mainly of Fire modules and it achieves AlexNet-level
accuracy on ImageNet with 50x fewer parameters. SqueezeNet can offer at least three advantages: (1) Smaller CNNs require
less communication across servers during distributed training. (2) Smaller CNNs require less bandwidth to export a new
model from the cloud to an autonomous car. (3) Smaller CNNs are more feasible to deploy on FPGAs and other hardware with
limited memory. Additionally, with model compression techniques, SqueezeNet is able to be compressed to less than
0.5MB (510× smaller than AlexNet). Blow is macroarchitectural view of SqueezeNet architecture. Left: SqueezeNet ;
Middle: SqueezeNet with simple bypass; Right: SqueezeNet with complex bypass .

Benchmark

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Model	Context	Top-1 (%)	Top-5 (%)	Params (M)	Train T.	Infer T.	Download	Config	Log
squeezenet_1.0	GPU	59.49	81.22				model	cfg	log
squeezenet_1.1	GPU	58.99	80.99				model	cfg	log

Notes

• All models are trained on ImageNet-1K training set and the top-1 accuracy is reported on the validatoin set.
• Context: GPU_TYPE x pieces - G/F, G - graph mode, F - pynative mode with ms function.

Examples

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Train

• The yaml config files that yield competitive results on ImageNet for different models are listed in
  the configs folder. To trigger training using preset yaml config.

  coming soon
  

• Here is the example for finetuning a pretrained squeezenet_1.0 on CIFAR10 dataset using Adam optimizer.

  python train.py --model=squeezenet1_0 --pretrained --opt=momentum --lr=0.001 dataset=cifar10 --num_classes=10 --dataset_download
  

Detailed adjustable parameters and their default value can be seen in config.py.

Eval

• To validate the model, you can use validate.py. Here is an example for squeezenet_1.0 to verify the accuracy of
  pretrained weights.

  python validate.py --model=squeezenet1_0 --dataset=imagenet --val_split=val --pretrained
  

• To validate the model, you can use validate.py. Here is an example for squeezenet_1.0 to verify the accuracy of your
  training.

  python validate.py --model=squeezenet1_0 --dataset=imagenet --val_split=val --ckpt_path='./ckpt/squeezenet1_0-best.ckpt'