路缘石图像分类模型
kerascv的Python项目详细描述
基于keras的大规模图像分类模型
这是一组大规模的图像分类模型。其中许多是预先训练过的
ImageNet-1K数据集，并在使用期间自动加载。所有的预训练模型都需要
同样的正常化。培训/评估/转换模型的脚本位于
^{
实施模型列表
- AlexNet（'One weird trick for parallelizing convolutional neural networks'）
- VGG/BN-VGG（'Very Deep Convolutional Networks for Large-Scale Image Recognition'）
- resnet（'Deep Residual Learning for Image Recognition'）
- preresnet（'Identity Mappings in Deep Residual Networks'）
- resnext（'Aggregated Residual Transformations for Deep Neural Networks'）
- senet/se resnet/se preresnet/se resnext（'Squeeze-and-Excitation Networks'）
- 登塞内特（'Densely Connected Convolutional Networks'）
- 黑暗参考号/tiny/19（'Darknet: Open source neural networks in c'）
- 黑暗-53（'YOLOv3: An Incremental Improvement'）
- 挤压网/挤压网（'SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size'）
- 挤压下一个（'SqueezeNext: Hardware-Aware Neural Network Design'）
- shufflenet（'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices'）
- shufflenetv2（'ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design'）
- 菜单（'Merging and Evolution: Improving Convolutional Neural Networks for Mobile Applications'）
- mobilenet（'MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications'）
- fd mobilenet（'FD-MobileNet: Improved MobileNet with A Fast Downsampling Strategy'）
- mobilenetv2（'MobileNetV2: Inverted Residuals and Linear Bottlenecks'）
- igcv3（'IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks'）
- mnasnet（'MnasNet: Platform-Aware Neural Architecture Search for Mobile'）
- 效率网（'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks'）
安装
要在项目中使用这些模型，只需使用所需的后端安装kerascv包。例如，对于MXNET后端：
pip install mxnet>=1.2.1 keras-mxnet kerascv
或者如果您喜欢TensorFlow后端：
pip install tensorflow kerascv
要启用/禁用不同的硬件支持，请检查相应后端的安装说明。
安装后，检查backend字段是否设置为文件~/.keras/keras.json中的正确值。它是
在使用mxnet后端的情况下，最好将image_data_format字段的值设置为channels_first。
用法
使用预训练resnet-18模型的示例（用于channels_first数据格式）：
from kerascv.model_provider import get_model as kecv_get_model
import numpy as np
net = kecv_get_model("resnet18", pretrained=True)
x = np.zeros((1, 3, 224, 224), np.float32)
y = net.predict(x)
预训练模型（imagenet-1k）
一些备注：
- 所有质量值都是用mxnet后端估计的。
- top1/top5是imagenet-1k数据集验证子集上的标准1-crop top-1/top-5错误（百分比）。
- flops/2是与mac的数量相似的flops的数量除以2。
- 备注
Converted from GL model表示模型在MXNet/Gluon上进行训练，然后转换为keras。
| Model | Top1 | Top5 | Params | FLOPs/2 | Remarks |
|---|---|---|---|---|---|
| AlexNet | 40.47 | 17.88 | 62,378,344 | 1,132.33M | Converted from GL model (log) |
| AlexNet-b | 41.08 | 18.53 | 61,100,840 | 714.83M | Converted from GL model (log) |
| ZFNet | 39.56 | 17.15 | 62,357,608 | 1,170.33M | Converted from GL model (log) |
| ZFNet-b | 36.30 | 14.83 | 107,627,624 | 2,479.13M | Converted from GL model (log) |
| VGG-11 | 29.59 | 10.16 | 132,863,336 | 7,615.87M | Converted from GL model (log) |
| VGG-13 | 28.37 | 9.50 | 133,047,848 | 11,317.65M | Converted from GL model (log) |
| VGG-16 | 26.61 | 8.32 | 138,357,544 | 15,480.10M | Converted from GL model (log) |
| VGG-19 | 25.87 | 8.23 | 143,667,240 | 19,642.55M | From dmlc/gluon-cv (log) |
| BN-VGG-11 | 28.55 | 9.34 | 132,866,088 | 7,630.21M | Converted from GL model (log) |
| BN-VGG-13 | 27.68 | 8.87 | 133,050,792 | 11,341.62M | Converted from GL model (log) |
| BN-VGG-16 | 25.50 | 7.57 | 138,361,768 | 15,506.38M | Converted from GL model (log) |
| BN-VGG-19 | 23.91 | 6.89 | 143,672,744 | 19,671.15M | Converted from GL model (log) |
| BN-VGG-11b | 30.34 | 10.57 | 132,868,840 | 7,630.72M | From dmlc/gluon-cv (log) |
| BN-VGG-13b | 29.48 | 10.16 | 133,053,736 | 11,342.14M | From dmlc/gluon-cv (log) |
| BN-VGG-16b | 26.88 | 8.65 | 138,365,992 | 15,507.20M | From dmlc/gluon-cv (log) |
| BN-VGG-19b | 25.65 | 8.14 | 143,678,248 | 19,672.26M | From dmlc/gluon-cv (log) |
| ResNet-10 | 34.59 | 13.85 | 5,418,792 | 894.04M | Converted from GL model (log) |
| ResNet-12 | 33.43 | 13.03 | 5,492,776 | 1,126.25M | Converted from GL model (log) |
| ResNet-14 | 32.18 | 12.20 | 5,788,200 | 1,357.94M | Converted from GL model (log) |
| ResNet-BC-14b | 30.25 | 11.16 | 10,064,936 | 1,479.12M | Converted from GL model (log) |
| ResNet-16 | 30.23 | 10.88 | 6,968,872 | 1,589.34M | Converted from GL model (log) |
| ResNet-18 x0.25 | 39.30 | 17.41 | 3,937,400 | 270.94M | Converted from GL model (log) |
| ResNet-18 x0.5 | 33.40 | 12.83 | 5,804,296 | 608.70M | Converted from GL model (log) |
| ResNet-18 x0.75 | 29.98 | 10.66 | 8,476,056 | 1,129.45M | Converted from GL model (log) |
| ResNet-18 | 28.08 | 9.52 | 11,689,512 | 1,820.41M | Converted from GL model (log) |
| ResNet-26 | 26.12 | 8.37 | 17,960,232 | 2,746.79M | Converted from GL model (log) |
| ResNet-BC-26b | 24.85 | 7.59 | 15,995,176 | 2,356.67M | Converted from GL model (log) |
| ResNet-34 | 24.53 | 7.44 | 21,797,672 | 3,672.68M | Converted from GL model (log) |
| ResNet-BC-38b | 23.48 | 6.72 | 21,925,416 | 3,234.21M | Converted from GL model (log) |
| ResNet-50 | 22.14 | 6.04 | 25,557,032 | 3,877.95M | Converted from GL model (log) |
| ResNet-50b | 22.06 | 6.10 | 25,557,032 | 4,110.48M | Converted from GL model (log) |
| ResNet-101 | 21.64 | 5.99 | 44,549,160 | 7,597.95M | From dmlc/gluon-cv (log) |
| ResNet-101b | 20.25 | 5.11 | 44,549,160 | 7,830.48M | Converted from GL model (log) |
| ResNet-152 | 20.74 | 5.35 | 60,192,808 | 11,321.85M | From dmlc/gluon-cv (log) |
| ResNet-152b | 19.63 | 4.79 | 60,192,808 | 11,554.38M | Converted from GL model (log) |
| PreResNet-10 | 34.65 | 14.01 | 5,417,128 | 894.19M | Converted from GL model (log) |
| PreResNet-12 | 33.56 | 13.22 | 5,491,112 | 1,126.40M | Converted from GL model (log) |
| PreResNet-14 | 32.29 | 12.19 | 5,786,536 | 1,358.09M | Converted from GL model (log) |
| PreResNet-BC-14b | 30.66 | 11.51 | 10,057,384 | 1,476.62M | Converted from GL model (log) |
| PreResNet-16 | 30.21 | 10.81 | 6,967,208 | 1,589.49M | Converted from GL model (log) |
| PreResNet-18 x0.25 | 39.63 | 17.78 | 3,935,960 | 270.93M | Converted from GL model (log) |
| PreResNet-18 x0.5 | 33.67 | 13.19 | 5,802,440 | 608.73M | Converted from GL model (log) |
| PreResNet-18 x0.75 | 29.95 | 10.68 | 8,473,784 | 1,129.51M | Converted from GL model (log) |
| PreResNet-18 | 28.16 | 9.52 | 11,687,848 | 1,820.56M | Converted from GL model (log) |
| PreResNet-26 | 26.02 | 8.34 | 17,958,568 | 2,746.94M | Converted from GL model (log) |
| PreResNet-BC-26b | 25.20 | 7.86 | 15,987,624 | 2,354.16M | Converted from GL model (log) |
| PreResNet-34 | 24.55 | 7.51 | 21,796,008 | 3,672.83M | Converted from GL model (log) |
| PreResNet-BC-38b | 22.65 | 6.33 | 21,917,864 | 3,231.70M | Converted from GL model (log) |
| PreResNet-50 | 22.26 | 6.20 | 25,549,480 | 3,875.44M | Converted from GL model (log) |
| PreResNet-50b | 22.35 | 6.32 | 25,549,480 | 4,107.97M | Converted from GL model (log) |
| PreResNet-101 | 21.43 | 5.75 | 44,541,608 | 7,595.44M | From dmlc/gluon-cv (log) |
| PreResNet-101b | 20.84 | 5.40 | 44,541,608 | 7,827.97M | Converted from GL model (log) |
| PreResNet-152 | 20.69 | 5.31 | 60,185,256 | 11,319.34M | From dmlc/gluon-cv (log) |
| PreResNet-152b | 19.89 | 5.00 | 60,185,256 | 11,551.87M | Converted from GL model (log) |
| PreResNet-200b | 21.09 | 5.64 | 64,666,280 | 15,068.63M | From tornadomeet/ResNet (log) |
| PreResNet-269b | 20.71 | 5.56 | 102,065,832 | 20,101.11M | From soeaver/mxnet-model (log) |
| ResNeXt-14 (16x4d) | 31.65 | 12.24 | 7,127,336 | 1,045.77M | Converted from GL model (log) |
| ResNeXt-14 (32x2d) | 32.15 | 12.46 | 7,029,416 | 1,031.32M | Converted from GL model (log) |
| ResNeXt-14 (32x4d) | 29.95 | 11.10 | 9,411,880 | 1,603.46M | Converted from GL model (log) |
| ResNeXt-26 (32x2d) | 26.34 | 8.50 | 9,924,136 | 1,461.06M | Converted from GL model (log) |
| ResNeXt-26 (32x4d) | 23.91 | 7.20 | 15,389,480 | 2,488.07M | Converted from GL model (log) |
| ResNeXt-101 (32x4d) | 21.30 | 5.78 | 44,177,704 | 8,003.45M | From Cadene/pretrained...pytorch (log) |
| ResNeXt-101 (64x4d) | 20.59 | 5.41 | 83,455,272 | 15,500.27M | From Cadene/pretrained...pytorch (log) |
| SE-ResNet-10 | 33.55 | 13.29 | 5,463,332 | 894.27M | Converted from GL model (log) |
| SE-ResNet-18 | 27.95 | 9.20 | 11,778,592 | 1,820.88M | Converted from GL model (log) |
| SE-ResNet-26 | 25.42 | 8.03 | 18,093,852 | 2,747.49M | Converted from GL model (log) |
| SE-ResNet-BC-26b | 23.44 | 6.82 | 17,395,976 | 2,359.58M | Converted from GL model (log) |
| SE-ResNet-BC-38b | 21.44 | 5.75 | 24,026,616 | 3,238.58M | Converted from GL model (log) |
| SE-ResNet-50 | 22.50 | 6.43 | 28,088,024 | 3,880.49M | From Cadene/pretrained...pytorch (log) |
| SE-ResNet-50b | 20.58 | 5.33 | 28,088,024 | 4,115.78M | Converted from GL model (log) |
| SE-ResNet-101 | 21.92 | 5.88 | 49,326,872 | 7,602.76M | From Cadene/pretrained...pytorch (log) |
| SE-ResNet-152 | 21.46 | 5.77 | 66,821,848 | 11,328.52M | From Cadene/pretrained...pytorch (log) |
| SE-PreResNet-10 | 33.60 | 13.06 | 5,461,668 | 894.42M | Converted from GL model (log) |
| SE-PreResNet-18 | 27.67 | 9.38 | 11,776,928 | 1,821.03M | Converted from GL model (log) |
| SE-PreResNet-BC-26b | 22.95 | 6.36 | 17,388,424 | 2,357.07M | Converted from GL model (log) |
| SE-ResNeXt-50 (32x4d) | 21.05 | 5.57 | 27,559,896 | 4,258.40M | From Cadene/pretrained...pytorch (log) |
| SE-ResNeXt-101 (32x4d) | 19.98 | 4.99 | 48,955,416 | 8,008.26M | From Cadene/pretrained...pytorch (log) |
| SENet-16 | 25.34 | 8.06 | 31,366,168 | 5,081.30M | Converted from GL model (log) |
| SENet-28 | 21.68 | 5.91 | 36,453,768 | 5,732.71M | Converted from GL model (log) |
| SENet-154 | 18.83 | 4.65 | 115,088,984 | 20,745.78M | From Cadene/pretrained...pytorch (log) |
| DenseNet-121 | 23.23 | 6.84 | 7,978,856 | 2,872.13M | Converted from GL model (log) |
| DenseNet-161 | 22.39 | 6.18 | 28,681,000 | 7,793.16M | From dmlc/gluon-cv (log) |
| DenseNet-169 | 22.64 | 6.55 | 14,149,480 | 3,403.89M | Converted from GL model (log) |
| DenseNet-201 | 22.69 | 6.35 | 20,013,928 | 4,347.15M | From dmlc/gluon-cv (log) |
| DarkNet Tiny | 40.31 | 17.46 | 1,042,104 | 500.85M | Converted from GL model (log) |
| DarkNet Ref | 37.99 | 16.68 | 7,319,416 | 367.59M | Converted from GL model (log) |
| DarkNet-53 | 21.43 | 5.56 | 41,609,928 | 7,133.86M | From dmlc/gluon-cv (log) |
| SqueezeNet v1.0 | 39.17 | 17.56 | 1,248,424 | 823.67M | Converted from GL model (log) |
| SqueezeNet v1.1 | 39.08 | 17.39 | 1,235,496 | 352.02M | Converted from GL model (log) |
| SqueezeResNet v1.0 | 39.40 | 17.80 | 1,248,424 | 823.67M | Converted from GL model (log) |
| SqueezeResNet v1.1 | 39.82 | 17.84 | 1,235,496 | 352.02M | Converted from GL model (log) |
| 1.0-SqNxt-23 | 42.28 | 18.62 | 724,056 | 287.28M | Converted from GL model (log) |
| 1.0-SqNxt-23v5 | 40.38 | 17.57 | 921,816 | 285.82M | Converted from GL model (log) |
| 1.5-SqNxt-23 | 34.59 | 13.30 | 1,511,824 | 552.39M | Converted from GL model (log) |
| 1.5-SqNxt-23v5 | 33.56 | 12.84 | 1,953,616 | 550.97M | Converted from GL model (log) |
| 2.0-SqNxt-23 | 30.15 | 10.66 | 2,583,752 | 898.48M | Converted from GL model (log) |
| 2.0-SqNxt-23v5 | 29.40 | 10.28 | 3,366,344 | 897.60M | Converted from GL model (log) |
| ShuffleNet x0.25 (g=1) | 62.00 | 36.76 | 209,746 | 12.35M | Converted from GL model (log) |
| ShuffleNet x0.25 (g=3) | 61.32 | 36.15 | 305,902 | 13.09M | Converted from GL model (log) |
| ShuffleNet x0.5 (g=1) | 46.21 | 22.38 | 534,484 | 41.16M | Converted from GL model (log) |
| ShuffleNet x0.5 (g=3) | 43.82 | 20.60 | 718,324 | 41.70M | Converted from GL model (log) |
| ShuffleNet x0.75 (g=1) | 39.24 | 16.75 | 975,214 | 86.42M | Converted from GL model (log) |
| ShuffleNet x0.75 (g=3) | 37.81 | 16.09 | 1,238,266 | 85.82M | Converted from GL model (log) |
| ShuffleNet x1.0 (g=1) | 34.41 | 13.50 | 1,531,936 | 148.13M | Converted from GL model (log) |
| ShuffleNet x1.0 (g=2) | 33.97 | 13.32 | 1,733,848 | 147.60M | Converted from GL model (log) |
| ShuffleNet x1.0 (g=3) | 33.96 | 13.29 | 1,865,728 | 145.46M | Converted from GL model (log) |
| ShuffleNet x1.0 (g=4) | 33.83 | 13.10 | 1,968,344 | 143.33M | Converted from GL model (log) |
| ShuffleNet x1.0 (g=8) | 33.64 | 13.20 | 2,434,768 | 150.76M | Converted from GL model (log) |
| ShuffleNetV2 x0.5 | 40.76 | 18.40 | 1,366,792 | 43.31M | Converted from GL model (log) |
| ShuffleNetV2 x1.0 | 31.02 | 11.33 | 2,278,604 | 149.72M | Converted from GL model (log) |
| ShuffleNetV2 x1.5 | 27.32 | 9.27 | 4,406,098 | 320.77M | Converted from GL model (log) |
| ShuffleNetV2 x2.0 | 25.77 | 8.22 | 7,601,686 | 595.84M | Converted from GL model (log) |
| ShuffleNetV2b x0.5 | 39.81 | 17.83 | 1,366,792 | 43.31M | Converted from GL model (log) |
| ShuffleNetV2b x1.0 | 30.38 | 11.01 | 2,279,760 | 150.62M | Converted from GL model (log) |
| ShuffleNetV2b x1.5 | 26.89 | 8.80 | 4,410,194 | 323.98M | Converted from GL model (log) |
| ShuffleNetV2b x2.0 | 25.18 | 8.10 | 7,611,290 | 603.37M | Converted from GL model (log) |
| 108-MENet-8x1 (g=3) | 43.61 | 20.31 | 654,516 | 42.68M | Converted from GL model (log) |
| 128-MENet-8x1 (g=4) | 42.08 | 19.14 | 750,796 | 45.98M | Converted from GL model (log) |
| 160-MENet-8x1 (g=8) | 43.47 | 20.28 | 850,120 | 45.63M | Converted from GL model (log) |
| 228-MENet-12x1 (g=3) | 33.85 | 12.88 | 1,806,568 | 152.93M | Converted from GL model (log) |
| 256-MENet-12x1 (g=4) | 32.22 | 12.17 | 1,888,240 | 150.65M | Converted from GL model (log) |
| 348-MENet-12x1 (g=3) | 27.85 | 9.36 | 3,368,128 | 312.00M | Converted from GL model (log) |
| 352-MENet-12x1 (g=8) | 31.29 | 11.67 | 2,272,872 | 157.35M | Converted from GL model (log) |
| 456-MENet-24x1 (g=3) | 25.00 | 7.80 | 5,304,784 | 567.90M | Converted from GL model (log) |
| MobileNet x0.25 | 45.80 | 22.17 | 470,072 | 44.09M | Converted from GL model (log) |
| MobileNet x0.5 | 33.94 | 13.30 | 1,331,592 | 155.42M | Converted from GL model (log) |
| MobileNet x0.75 | 29.85 | 10.51 | 2,585,560 | 333.99M | Converted from GL model (log) |
| MobileNet x1.0 | 26.43 | 8.66 | 4,231,976 | 579.80M | Converted from GL model (log) |
| FD-MobileNet x0.25 | 55.42 | 30.52 | 383,160 | 12.95M | Converted from GL model (log) |
| FD-MobileNet x0.5 | 42.61 | 19.69 | 993,928 | 41.84M | Converted from GL model (log) |
| FD-MobileNet x0.75 | 37.90 | 16.01 | 1,833,304 | 86.68M | Converted from GL model (log) |
| FD-MobileNet x1.0 | 33.80 | 13.12 | 2,901,288 | 147.46M | Converted from GL model (log) |
| MobileNetV2 x0.25 | 48.06 | 24.12 | 1,516,392 | 34.24M | Converted from GL model (log) |
| MobileNetV2 x0.5 | 35.63 | 14.43 | 1,964,736 | 100.13M | Converted from GL model (log) |
| MobileNetV2 x0.75 | 29.76 | 10.44 | 2,627,592 | 198.50M | Converted from GL model (log) |
| MobileNetV2 x1.0 | 26.76 | 8.64 | 3,504,960 | 329.36M | Converted from GL model (log) |
| IGCV3 x0.25 | 53.41 | 28.29 | 1,534,020 | 41.29M | Converted from GL model (log) |
| IGCV3 x0.5 | 39.39 | 17.04 | 1,985,528 | 111.12M | Converted from GL model (log) |
| IGCV3 x0.75 | 30.71 | 10.97 | 2,638,084 | 210.95M | Converted from GL model (log) |
| IGCV3 x1.0 | 27.72 | 8.99 | 3,491,688 | 340.79M | Converted from GL model (log) |
| MnasNet | 31.30 | 11.45 | 4,308,816 | 317.67M | From zeusees/Mnasnet...Model (log) |
| EfficientNet-B0 | 24.50 | 7.22 | 5,288,548 | 414.31M | Converted from GL model (log) |
| EfficientNet-B1 | 22.89 | 6.26 | 7,794,184 | 732.54M | Converted from GL model (log) |
| EfficientNet-B0b | 22.95 | 6.69 | 5,288,548 | 414.31M | From rwightman/pyt...models (log) |
| EfficientNet-B1b | 20.97 | 5.64 | 7,794,184 | 732.54M | From rwightman/pyt...models (log) |
| EfficientNet-B2b | 19.93 | 5.16 | 9,109,994 | 1,051.98M | From rwightman/pyt...models (log) |
| EfficientNet-B3b | 18.59 | 4.31 | 12,233,232 | 1,928.55M | From rwightman/pyt...models (log) |
| EfficientNet-B4b | 17.24 | 3.76 | 19,341,616 | 4,607.46M | From rwightman/pyt...models (log) |
| EfficientNet-B5b | 16.39 | 3.34 | 30,389,784 | 10,695.20M | From rwightman/pyt...models (log) |
| EfficientNet-B6b | 15.96 | 3.12 | 43,040,704 | 19,796.24M | From rwightman/pyt...models (log) |
| EfficientNet-B7b | 15.70 | 3.11 | 66,347,960 | 39,010.98M | From rwightman/pyt...models (log) |
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