|
1 | | -""" Generic EfficientNets |
| 1 | +""" PyTorch EfficientNet Family |
| 2 | +
|
| 3 | +An implementation of EfficienNet that covers variety of related models with efficient architectures: |
| 4 | +
|
| 5 | +* EfficientNet (B0-B8 + Tensorflow pretrained AutoAug/RandAug/AdvProp weight ports) |
| 6 | + - EfficientNet: Rethinking Model Scaling for CNNs - https://arxiv.org/abs/1905.11946 |
| 7 | + - CondConv: Conditionally Parameterized Convolutions for Efficient Inference - https://arxiv.org/abs/1904.04971 |
| 8 | + - Adversarial Examples Improve Image Recognition - https://arxiv.org/abs/1911.09665 |
2 | 9 |
|
3 | | -A generic class with building blocks to support a variety of models with efficient architectures: |
4 | | -* EfficientNet (B0-B7) |
5 | | -* EfficientNet-EdgeTPU |
6 | | -* EfficientNet-CondConv |
7 | 10 | * MixNet (Small, Medium, and Large) |
8 | | -* MnasNet B1, A1 (SE), Small |
9 | | -* MobileNet V1, V2, and V3 |
| 11 | + - MixConv: Mixed Depthwise Convolutional Kernels - https://arxiv.org/abs/1907.09595 |
| 12 | +
|
| 13 | +* MNasNet B1, A1 (SE), Small |
| 14 | + - MnasNet: Platform-Aware Neural Architecture Search for Mobile - https://arxiv.org/abs/1807.11626 |
| 15 | +
|
10 | 16 | * FBNet-C |
| 17 | + - FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable NAS - https://arxiv.org/abs/1812.03443 |
| 18 | +
|
11 | 19 | * Single-Path NAS Pixel1 |
12 | | -* And likely more... |
| 20 | + - Single-Path NAS: Designing Hardware-Efficient ConvNets - https://arxiv.org/abs/1904.02877 |
13 | 21 |
|
14 | | -TODO not all combinations and variations have been tested. Currently working on training hyper-params... |
| 22 | +* And likely more... |
15 | 23 |
|
16 | 24 | Hacked together by Ross Wightman |
17 | 25 | """ |
@@ -183,8 +191,6 @@ def _cfg(url='', **kwargs): |
183 | 191 | url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_l-6c92e0c8.pth'), |
184 | 192 | } |
185 | 193 |
|
186 | | - |
187 | | - |
188 | 194 | _DEBUG = False |
189 | 195 |
|
190 | 196 |
|
|
0 commit comments