ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
Currently, the neural network architecture design is mostly guided by the indirect metric of computation complexity, i.e., FLOPs. However, the direct metric, e.g., speed, also depends on the other factors such as memory access cost and platform characterics. Thus, this work proposes to evaluate the direct metric on the target platform, beyond only considering FLOPs. Based on a series of controlled experiments, this work derives several practical guidelines for efficient network design. Accordingly, a new architecture is presented, called ShuffleNet V2. Comprehensive ablation experiments verify that our model is the state-of-the-art in terms of speed and accuracy tradeoff.
Predict image
from mmpretrain import inference_model
predict = inference_model('shufflenet-v2-1x_16xb64_in1k', 'demo/bird.JPEG')
print(predict['pred_class'])
print(predict['pred_score'])Use the model
import torch
from mmpretrain import get_model
model = get_model('shufflenet-v2-1x_16xb64_in1k', pretrained=True)
inputs = torch.rand(1, 3, 224, 224)
out = model(inputs)
print(type(out))
# To extract features.
feats = model.extract_feat(inputs)
print(type(feats))Train/Test Command
Prepare your dataset according to the docs.
Train:
python tools/train.py configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.pyTest:
python tools/test.py configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200812-5bf4721e.pth| Model | Pretrain | Params (M) | Flops (G) | Top-1 (%) | Top-5 (%) | Config | Download |
|---|---|---|---|---|---|---|---|
shufflenet-v2-1x_16xb64_in1k |
From scratch | 2.28 | 0.15 | 69.55 | 88.92 | config | model | log |
@inproceedings{ma2018shufflenet,
title={Shufflenet v2: Practical guidelines for efficient cnn architecture design},
author={Ma, Ningning and Zhang, Xiangyu and Zheng, Hai-Tao and Sun, Jian},
booktitle={Proceedings of the European conference on computer vision (ECCV)},
pages={116--131},
year={2018}
}