ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
2018-07-30ECCV 2018Code Available1· sign in to hype
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, Jian Sun
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/savageyusuff/MobilePose-Pipytorch★ 16
- github.com/Yang-YiFan/DiracDeltaNetpytorch★ 0
- github.com/PaulGitt/ShuffleNetV2-tensorflowtf★ 0
- github.com/ba-san/MobilePose-Pipytorch★ 0
- github.com/forcefulowl/image_classificationtf★ 0
- github.com/emilianavt/OpenSeeFacetf★ 0
- github.com/megvii-research/basecls/tree/main/zoo/public/snetnone★ 0
- github.com/mindlab-ai/mindcv/blob/main/mindcv/models/shufflenetv2.pymindspore★ 0
- github.com/Qengineering/ShuffleNetV2-ncnnnone★ 0
- github.com/opconty/keras-shufflenetV2none★ 0
Abstract
Datasets, Transforms and Models specific to Computer Vision
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| ImageNet | ShuffleNet V2 | Top 1 Accuracy | 75.4 | — | Unverified |