tensorflowcv 0.0.36

基于tensorflow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 ^{}回购。

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'）
• 信道网（'ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions'）
• 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'）

安装

要在项目中使用这些模型，只需使用tensorflow：

安装tensorflowcv包

pip install tensorflowcv tensorflow>=1.11.0

要启用/禁用不同的硬件支持，请签出tensorflow installationinstructions。

用法

使用预训练resnet-18模型的示例（使用channels_first数据格式）：

from tensorflowcv.model_provider import get_model as tfcv_get_model
from tensorflowcv.model_provider import init_variables_from_state_dict as tfcv_init_variables_from_state_dict
import tensorflow as tf
import numpy as np

net = tfcv_get_model("resnet18", pretrained=True, data_format="channels_first")
x = tf.placeholder(dtype=tf.float32, shape=(None, 3, 224, 224), name="xx")
y_net = net(x)

with tf.Session() as sess:
    tfcv_init_variables_from_state_dict(sess=sess, state_dict=net.state_dict)
    x_value = np.zeros((1, 3, 224, 224), np.float32)
    y = sess.run(y_net, feed_dict={x: x_value})

预训练模型（imagenet-1k）

一些备注：

• 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tensorflow。

Model	Top1	Top5	Params	FLOPs/2	Remarks
AlexNet	40.44	17.88	62,378,344	1,132.33M	Converted from GL model (log)
AlexNet-b	41.05	18.53	61,100,840	714.83M	Converted from GL model (log)
ZFNet	39.51	17.15	62,357,608	1,170.33M	Converted from GL model (log)
ZFNet-b	36.30	14.82	107,627,624	2,479.13M	Converted from GL model (log)
VGG-11	29.58	10.15	132,863,336	7,615.87M	Converted from GL model (log)
VGG-13	28.34	9.46	133,047,848	11,317.65M	Converted from GL model (log)
VGG-16	26.65	8.30	138,357,544	15,480.10M	Converted from GL model (log)
VGG-19	25.93	8.23	143,667,240	19,642.55M	From dmlc/gluon-cv (log)
BN-VGG-11	28.55	9.36	132,866,088	7,630.21M	Converted from GL model (log)
BN-VGG-13	27.73	8.88	133,050,792	11,341.62M	Converted from GL model (log)
BN-VGG-16	25.48	7.55	138,361,768	15,506.38M	Converted from GL model (log)
BN-VGG-19	23.88	6.89	143,672,744	19,671.15M	Converted from GL model (log)
BN-VGG-11b	30.34	10.58	132,868,840	7,630.72M	From dmlc/gluon-cv (log)
BN-VGG-13b	29.47	10.15	133,053,736	11,342.14M	From dmlc/gluon-cv (log)
BN-VGG-16b	26.83	8.66	138,365,992	15,507.20M	From dmlc/gluon-cv (log)
BN-VGG-19b	25.62	8.17	143,678,248	19,672.26M	From dmlc/gluon-cv (log)
ResNet-10	34.62	13.90	5,418,792	894.04M	Converted from GL model (log)
ResNet-12	33.36	13.00	5,492,776	1,126.25M	Converted from GL model (log)
ResNet-14	32.22	12.25	5,788,200	1,357.94M	Converted from GL model (log)
ResNet-BC-14b	30.25	11.21	10,064,936	1,479.12M	Converted from GL model (log)
ResNet-16	30.25	10.86	6,968,872	1,589.34M	Converted from GL model (log)
ResNet-18 x0.25	39.32	17.41	3,937,400	270.94M	Converted from GL model (log)
ResNet-18 x0.5	33.36	12.87	5,804,296	608.70M	Converted from GL model (log)
ResNet-18 x0.75	30.00	10.69	8,476,056	1,129.45M	Converted from GL model (log)
ResNet-18	28.16	9.56	11,689,512	1,820.41M	Converted from GL model (log)
ResNet-26	26.13	8.38	17,960,232	2,746.79M	Converted from GL model (log)
ResNet-BC-26b	24.81	7.57	15,995,176	2,356.67M	Converted from GL model (log)
ResNet-34	24.57	7.42	21,797,672	3,672.68M	Converted from GL model (log)
ResNet-BC-38b	23.51	6.73	21,925,416	3,234.21M	Converted from GL model (log)
ResNet-50	22.14	6.05	25,557,032	3,877.95M	Converted from GL model (log)
ResNet-50b	22.03	6.09	25,557,032	4,110.48M	Converted from GL model (log)
ResNet-101	21.61	6.01	44,549,160	7,597.95M	From dmlc/gluon-cv (log)
ResNet-101b	20.29	5.07	44,549,160	7,830.48M	Converted from GL model (log)
ResNet-152	20.73	5.35	60,192,808	11,321.85M	From dmlc/gluon-cv (log)
ResNet-152b	19.61	4.85	60,192,808	11,554.38M	Converted from GL model (log)
PreResNet-10	34.71	14.01	5,417,128	894.19M	Converted from GL model (log)
PreResNet-12	33.58	13.21	5,491,112	1,126.40M	Converted from GL model (log)
PreResNet-14	32.30	12.16	5,786,536	1,358.09M	Converted from GL model (log)
PreResNet-BC-14b	30.80	11.53	10,057,384	1,476.62M	Converted from GL model (log)
PreResNet-16	30.23	10.82	6,967,208	1,589.49M	Converted from GL model (log)
PreResNet-18 x0.25	39.66	17.76	3,935,960	270.93M	Converted from GL model (log)
PreResNet-18 x0.5	33.73	13.18	5,802,440	608.73M	Converted from GL model (log)
PreResNet-18 x0.75	29.93	10.71	8,473,784	1,129.51M	Converted from GL model (log)
PreResNet-18	28.21	9.49	11,687,848	1,820.56M	Converted from GL model (log)
PreResNet-26	26.02	8.33	17,958,568	2,746.94M	Converted from GL model (log)
PreResNet-BC-26b	25.24	7.89	15,987,624	2,354.16M	Converted from GL model (log)
PreResNet-34	24.53	7.54	21,796,008	3,672.83M	Converted from GL model (log)
PreResNet-BC-38b	22.70	6.34	21,917,864	3,231.70M	Converted from GL model (log)
PreResNet-50	22.19	6.25	25,549,480	3,875.44M	Converted from GL model (log)
PreResNet-50b	22.36	6.31	25,549,480	4,107.97M	Converted from GL model (log)
PreResNet-101	21.49	5.72	44,541,608	7,595.44M	From dmlc/gluon-cv (log)
PreResNet-101b	20.80	5.39	44,541,608	7,827.97M	Converted from GL model (log)
PreResNet-152	20.63	5.29	60,185,256	11,319.34M	From dmlc/gluon-cv (log)
PreResNet-152b	19.87	5.00	60,185,256	11,551.87M	Converted from GL model (log)
PreResNet-200b	21.12	5.60	64,666,280	15,068.63M	From tornadomeet/ResNet (log)
PreResNet-269b	20.73	5.55	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)	32.11	12.46	7,029,416	1,031.32M	Converted from GL model (log)
ResNeXt-14 (32x4d)	29.95	11.13	9,411,880	1,603.46M	Converted from GL model (log)
ResNeXt-26 (32x2d)	26.29	8.49	9,924,136	1,461.06M	Converted from GL model (log)
ResNeXt-26 (32x4d)	23.94	7.17	15,389,480	2,488.07M	Converted from GL model (log)
ResNeXt-101 (32x4d)	21.33	5.80	44,177,704	8,003.45M	From Cadene/pretrained...pytorch (log)
ResNeXt-101 (64x4d)	20.59	5.43	83,455,272	15,500.27M	From Cadene/pretrained...pytorch (log)
SE-ResNet-10	33.56	13.36	5,463,332	894.27M	Converted from GL model (log)
SE-ResNet-18	27.89	9.23	11,778,592	1,820.88M	Converted from GL model (log)
SE-ResNet-26	25.44	8.09	18,093,852	2,747.49M	Converted from GL model (log)
SE-ResNet-BC-26b	23.44	6.81	17,395,976	2,359.58M	Converted from GL model (log)
SE-ResNet-BC-38b	21.43	5.78	24,026,616	3,238.58M	Converted from GL model (log)
SE-ResNet-50	22.53	6.43	28,088,024	3,880.49M	From Cadene/pretrained...pytorch (log)
SE-ResNet-50b	20.60	5.33	28,088,024	4,115.78M	Converted from GL model (log)
SE-ResNet-101	21.92	5.89	49,326,872	7,602.76M	From Cadene/pretrained...pytorch (log)
SE-ResNet-152	21.48	5.78	66,821,848	11,328.52M	From Cadene/pretrained...pytorch (log)
SE-PreResNet-10	33.67	13.09	5,461,668	894.42M	Converted from GL model (log)
SE-PreResNet-18	27.67	9.41	11,776,928	1,821.03M	Converted from GL model (log)
SE-PreResNet-BC-26b	22.96	6.34	17,388,424	2,357.07M	Converted from GL model (log)
SE-ResNeXt-50 (32x4d)	21.01	5.53	27,559,896	4,258.40M	From Cadene/pretrained...pytorch (log)
SE-ResNeXt-101 (32x4d)	19.99	4.97	48,955,416	8,008.26M	From Cadene/pretrained...pytorch (log)
SENet-16	25.34	8.03	31,366,168	5,081.30M	Converted from GL model (log)
SENet-28	21.68	5.94	36,453,768	5,732.71M	Converted from GL model (log)
SENet-154	18.77	4.63	115,088,984	20,745.78M	From Cadene/pretrained...pytorch (log)
DenseNet-121	23.21	6.88	7,978,856	2,872.13M	Converted from GL model (log)
DenseNet-161	22.40	6.17	28,681,000	7,793.16M	From dmlc/gluon-cv (log)
DenseNet-169	22.60	6.58	14,149,480	3,403.89M	Converted from GL model (log)
DenseNet-201	22.70	6.35	20,013,928	4,347.15M	From dmlc/gluon-cv (log)
DarkNet Tiny	40.35	17.51	1,042,104	500.85M	Converted from GL model (log)
DarkNet Ref	37.99	16.72	7,319,416	367.59M	Converted from GL model (log)
DarkNet-53	21.42	5.55	41,609,928	7,133.86M	From dmlc/gluon-cv (log)
SqueezeNet v1.0	39.18	17.58	1,248,424	823.67M	Converted from GL model (log)
SqueezeNet v1.1	39.14	17.39	1,235,496	352.02M	Converted from GL model (log)
SqueezeResNet v1.0	39.36	17.82	1,248,424	823.67M	Converted from GL model (log)
SqueezeResNet v1.1	39.75	17.92	1,235,496	352.02M	Converted from GL model (log)
1.0-SqNxt-23	45.41	21.08	724,056	287.28M	Converted from GL model (log)
1.0-SqNxt-23v5	44.68	20.77	921,816	285.82M	Converted from GL model (log)
1.5-SqNxt-23	37.11	15.09	1,511,824	552.39M	Converted from GL model (log)
1.5-SqNxt-23v5	37.33	15.39	1,953,616	550.97M	Converted from GL model (log)
2.0-SqNxt-23	32.44	12.35	2,583,752	898.48M	Converted from GL model (log)
2.0-SqNxt-23v5	32.19	12.13	3,366,344	897.60M	Converted from GL model (log)
ShuffleNet x0.25 (g=1)	62.03	36.80	209,746	12.35M	Converted from GL model (log)
ShuffleNet x0.25 (g=3)	61.33	36.17	305,902	13.09M	Converted from GL model (log)
ShuffleNet x0.5 (g=1)	46.25	22.31	534,484	41.16M	Converted from GL model (log)
ShuffleNet x0.5 (g=3)	43.89	20.63	718,324	41.70M	Converted from GL model (log)
ShuffleNet x0.75 (g=1)	39.26	16.78	975,214	86.42M	Converted from GL model (log)
ShuffleNet x0.75 (g=3)	37.89	16.13	1,238,266	85.82M	Converted from GL model (log)
ShuffleNet x1.0 (g=1)	34.48	13.51	1,531,936	148.13M	Converted from GL model (log)
ShuffleNet x1.0 (g=2)	33.97	13.33	1,733,848	147.60M	Converted from GL model (log)
ShuffleNet x1.0 (g=3)	33.93	13.32	1,865,728	145.46M	Converted from GL model (log)
ShuffleNet x1.0 (g=4)	33.89	13.13	1,968,344	143.33M	Converted from GL model (log)
ShuffleNet x1.0 (g=8)	33.65	13.21	2,434,768	150.76M	Converted from GL model (log)
ShuffleNetV2 x0.5	40.88	18.44	1,366,792	43.31M	Converted from GL model (log)
ShuffleNetV2 x1.0	31.02	11.31	2,278,604	149.72M	Converted from GL model (log)
ShuffleNetV2 x1.5	27.33	9.23	4,406,098	320.77M	Converted from GL model (log)
ShuffleNetV2 x2.0	25.80	8.21	7,601,686	595.84M	Converted from GL model (log)
ShuffleNetV2b x0.5	39.80	17.84	1,366,792	43.31M	Converted from GL model (log)
ShuffleNetV2b x1.0	30.40	11.04	2,279,760	150.62M	Converted from GL model (log)
ShuffleNetV2b x1.5	26.92	8.80	4,410,194	323.98M	Converted from GL model (log)
ShuffleNetV2b x2.0	25.20	8.10	7,611,290	603.37M	Converted from GL model (log)
108-MENet-8x1 (g=3)	43.67	20.32	654,516	42.68M	Converted from GL model (log)
128-MENet-8x1 (g=4)	42.04	19.15	750,796	45.98M	Converted from GL model (log)
160-MENet-8x1 (g=8)	43.53	20.28	850,120	45.63M	Converted from GL model (log)
228-MENet-12x1 (g=3)	33.85	12.92	1,806,568	152.93M	Converted from GL model (log)
256-MENet-12x1 (g=4)	32.19	12.19	1,888,240	150.65M	Converted from GL model (log)
348-MENet-12x1 (g=3)	27.87	9.35	3,368,128	312.00M	Converted from GL model (log)
352-MENet-12x1 (g=8)	31.31	11.69	2,272,872	157.35M	Converted from GL model (log)
456-MENet-24x1 (g=3)	24.96	7.79	5,304,784	567.90M	Converted from GL model (log)
MobileNet x0.25	45.78	22.21	470,072	44.09M	Converted from GL model (log)
MobileNet x0.5	33.85	13.31	1,331,592	155.42M	Converted from GL model (log)
MobileNet x0.75	29.82	10.49	2,585,560	333.99M	Converted from GL model (log)
MobileNet x1.0	26.45	8.67	4,231,976	579.80M	Converted from GL model (log)
FD-MobileNet x0.25	55.50	30.50	383,160	12.95M	Converted from GL model (log)
FD-MobileNet x0.5	42.67	19.70	993,928	41.84M	Converted from GL model (log)
FD-MobileNet x0.75	37.95	16.02	1,833,304	86.68M	Converted from GL model (log)
FD-MobileNet x1.0	33.78	13.18	2,901,288	147.46M	Converted from GL model (log)
MobileNetV2 x0.25	48.18	24.16	1,516,392	34.24M	Converted from GL model (log)
MobileNetV2 x0.5	35.56	14.46	1,964,736	100.13M	Converted from GL model (log)
MobileNetV2 x0.75	29.80	10.44	2,627,592	198.50M	Converted from GL model (log)
MobileNetV2 x1.0	26.79	8.62	3,504,960	329.36M	Converted from GL model (log)
IGCV3 x0.25	53.39	28.35	1,534,020	41.29M	Converted from GL model (log)
IGCV3 x0.5	39.38	17.05	1,985,528	111.12M	Converted from GL model (log)
IGCV3 x0.75	30.80	10.96	2,638,084	210.95M	Converted from GL model (log)
IGCV3 x1.0	27.67	9.03	3,491,688	340.79M	Converted from GL model (log)
MnasNet	31.29	11.44	4,308,816	317.67M	From zeusees/Mnasnet...Model (log)

标签：

• from
• 模型
• 图像
• log
• model
• 分类
• resnet
• converted
• gl

欢迎加入QQ群-->： 979659372

推荐PyPI第三方库

• giraffez

  面向python的用户友好的快速teradata客户端

• elastic-trade-server.articles

  电子商务服务器的条款目录组件

• django-query-parameters

  django templatetags简化了从querystring中创建、更新和删除查询参数的过程

• NEMO

  nemo是一个实验室物流web应用程序。使用它来安排预订、控制工具访问、跟踪维护问题等。

• py-mail

  以可读类型提供来自gmail邮箱的数据

• castle.theme

  CastleCMS主题包

• sovrin_installer

  Sovrin安装程序

• odoo12-addon-product-weight

  允许从其组件计算产品重量。

• collective.sphinx.autoatschema

  原型模式的sphinx扩展

• python-ceilometerclient

  OpenStack遥测API客户端库

• crawler-gen

  雷特洛克履带

• italianskin.templates

  plone的意大利语皮肤xhtml严格模板

• sorting

  python中的black实现

• obfsprox

  用python编写的可插入传输代理

• pywizard

  为服务器管理实现类chef/puppet方法的工具。