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https://github.com/d-li14/regnet.pytorch

PyTorch-style and human-readable RegNet with a spectrum of pre-trained models
https://github.com/d-li14/regnet.pytorch

imagenet neural-architecture-search pretrained-models pytorch regnet resnext

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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](https://arxiv.org/abs/2003.13678) by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Dollár.

Compared to the [official codebase](https://github.com/facebookresearch/pycls), this repository follows the [torchvision's ResNeXt](https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py) 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](https://hkustconnect-my.sharepoint.com/:u:/g/personal/dlibh_connect_ust_hk/EcDivRLCy7BHmVuoKVyqfZsB8t7OpFoCEdnLOD495UKWCw?e=r0h5fh) | 2.685 | 0.199 | 1024 | 68.210 | 68.9 |
| [RegNetX-400M](https://hkustconnect-my.sharepoint.com/:u:/g/personal/dlibh_connect_ust_hk/EaL_0Di7OK5DsCLtvGcw418BqZGg5BD875kOIFMnALcMLQ?e=1mEB0v) | 5.158 | 0.398 | 1024 | 72.278 | 72.7 |
| [RegNetX-600M](https://hkustconnect-my.sharepoint.com/:u:/g/personal/dlibh_connect_ust_hk/ETFwqAcWWctLh3dPaCm0R5YB4xJVvoGdCTuwYYQzJiq35g?e=8OeH7k) | 6.196 | 0.601 | 1024 | 73.862 | 74.1 |
| [RegNetX-800M](https://hkustconnect-my.sharepoint.com/:u:/g/personal/dlibh_connect_ust_hk/Ecd7nKqHLnZCmlgitigejWIBYLhcpqkDCoBx_CEILtQcCg?e=8Xt961) | 7.260 | 0.800 | 1024 | 74.940 | 75.2 |
| [RegNetX-1.6G](https://hkustconnect-my.sharepoint.com/:u:/g/personal/dlibh_connect_ust_hk/EXOeBD6xco5JmvLziY4zySEB1bR00A7DqCx9t4IbI_MAng?e=ZG5PxS) | 9.190 | 1.603 | 1024 | 76.706 | 77.0 |
| [RegNetX-3.2G](https://hkustconnect-my.sharepoint.com/:u:/g/personal/dlibh_connect_ust_hk/EQ1o8qVNLuhBg21Kgf_bss8BHFrhm8PLI3xMrMtD7a192Q?e=RG2LoH) | 15.296 | 3.177 | 512 | 78.188 | 78.3 |
| [RegNetX-4.0G](https://hkustconnect-my.sharepoint.com/:u:/g/personal/dlibh_connect_ust_hk/ET7rz66druZGqPe-IFC21MQBd_kcLoYwXIoR9YQbJpGOqA?e=wfYSsA) | 22.118 | 3.965 | 512 | 78.690 | 78.6 |
| [RegNetX-6.4G](https://hkustconnect-my.sharepoint.com/:u:/g/personal/dlibh_connect_ust_hk/EQkGuWBHehlDnkr3cASqgS4Btul3Lb_iuO4IGHIeHrkWbA?e=ndLLQs) | 26.209 | 6.460 | 512 | 79.152 | 79.2 |
| [RgeNetX-8.0G](https://hkustconnect-my.sharepoint.com/:u:/g/personal/dlibh_connect_ust_hk/EYTpeCq4OnNIr9ly3KmokywBodWSZHHBNPhiwirhk9Urag?e=PDsrFu) | 39.573 | 7.995 | 512 | 79.380 | 79.3 |
| [RegNetX-12G](https://hkustconnect-my.sharepoint.com/:u:/g/personal/dlibh_connect_ust_hk/EWEqa2PJVdxOj-M95XlLFVIBns9cnbdV6V6ASl-lyHzwyw?e=XVhG10) | 46.106 | 12.087 | 256 | 79.998 | 79.7 |
| [RegNetX-16G](https://hkustconnect-my.sharepoint.com/:u:/g/personal/dlibh_connect_ust_hk/ETjNce9S9bxGgU09rLXoXucBBNLbo3t8zdtyriK-Vc8Eww?e=CrNU6u) | 54.279 | 15.941 | 256 | 80.118 | 80.0 |
| [RegNetX-32G](https://hkustconnect-my.sharepoint.com/:u:/g/personal/dlibh_connect_ust_hk/EReWI0v2kvVBpKAGWRy2Hb0BKaIk6wx-VbkFBqYoE-YQZw?e=dT0dos) | 107.812 | 31.736 | 256 | 80.516 | 80.5 |

# Citation
```bibtex
@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}
}
```