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PyTorch-style and human-readable RegNet with a spectrum of pre-trained models

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RegNet Implementation with TorchVision Style

PyTorch implementation of Designing Network Design Spaces by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Dollár.

Compared to the official codebase, this repository follows the torchvision's ResNeXt style, which is expected to be more easily interpreted and utilized by pre-existing downstream applications.

We train the following models on 8x TITAN XP GPUs with 12G VRAM. During the first five epochs, we linearly ramp up the learning rate from 0.1.

Pre-trained Models

Model Params (M) GFLOPs Batch size Top-1 acc (%) (our impl.) Top-1 acc (%) (official)
RegNetX-200M 2.685 0.199 1024 68.210 68.9
RegNetX-400M 5.158 0.398 1024 72.278 72.7
RegNetX-600M 6.196 0.601 1024 73.862 74.1
RegNetX-800M 7.260 0.800 1024 74.940 75.2
RegNetX-1.6G 9.190 1.603 1024 76.706 77.0
RegNetX-3.2G 15.296 3.177 512 78.188 78.3
RegNetX-4.0G 22.118 3.965 512 78.690 78.6
RegNetX-6.4G 26.209 6.460 512 79.152 79.2
RgeNetX-8.0G 39.573 7.995 512 79.380 79.3
RegNetX-12G 46.106 12.087 256 79.998 79.7
RegNetX-16G 54.279 15.941 256 80.118 80.0
RegNetX-32G 107.812 31.736 256 80.516 80.5

Citation

@InProceedings{Radosavovic_2020_CVPR,
author = {Radosavovic, Ilija and Kosaraju, Raj Prateek and Girshick, Ross and He, Kaiming and Doll{\'a}r, Piotr},
title = {Designing Network Design Spaces},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}

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