caffe-segnet | Semantic Segmentation ; windows vs2013 python2
kandi X-RAY | caffe-segnet Summary
kandi X-RAY | caffe-segnet Summary
caffe-segnet is a C++ library. caffe-segnet has no bugs, it has no vulnerabilities and it has low support. However caffe-segnet has a Non-SPDX License. You can download it from GitHub.
windows vs2013 python2.7 matlab2014以上 添加upsample 、BN层。支持Segnet,添加Segnet-C++、python接口demo 打开windows-Caffe.sln编译即可,其他编译问题同微软官方caffe步骤 添加了cpm层,输入层数据可以旋转变换. 这个没有DenseImageDataLayer,不过可以如下代替: data 和label分开输入 layer { name: "data" type: "Data" top:"data" include { phase: TRAIN } transform_param { mean_file: "G:/interest_of_imags_for_recognation/VOC2012/Resize224/Img_train_mean.binaryproto" } data_param { source: "G:/interest_of_imags_for_recognation/VOC2012/Resize224/Img_train" batch_size: 1 backend: LMDB } } layer { name: "label" type: "Data" top:"label" include { phase: TRAIN } data_param { source: "G:/interest_of_imags_for_recognation/VOC2012/Resize224/Label_train" batch_size: 1 backend: LMDB } } layer { name: "data" type: "Data" top: "data" include { phase: TEST } transform_param { mean_file: "G:/interest_of_imags_for_recognation/VOC2012/Resize224/Img_val_mean.binaryproto" } data_param { source: "G:/interest_of_imags_for_recognation/VOC2012/Resize224/Img_val" batch_size: 1 backend: LMDB } } layer { name: "label" type: "Data" top: "label" include { phase: TEST } data_param { source: "G:/interest_of_imags_for_recognation/VOC2012/Resize224/Label_val" batch_size: 1 backend: LMDB } }.
windows vs2013 python2.7 matlab2014以上 添加upsample 、BN层。支持Segnet,添加Segnet-C++、python接口demo 打开windows-Caffe.sln编译即可,其他编译问题同微软官方caffe步骤 添加了cpm层,输入层数据可以旋转变换. 这个没有DenseImageDataLayer,不过可以如下代替: data 和label分开输入 layer { name: "data" type: "Data" top:"data" include { phase: TRAIN } transform_param { mean_file: "G:/interest_of_imags_for_recognation/VOC2012/Resize224/Img_train_mean.binaryproto" } data_param { source: "G:/interest_of_imags_for_recognation/VOC2012/Resize224/Img_train" batch_size: 1 backend: LMDB } } layer { name: "label" type: "Data" top:"label" include { phase: TRAIN } data_param { source: "G:/interest_of_imags_for_recognation/VOC2012/Resize224/Label_train" batch_size: 1 backend: LMDB } } layer { name: "data" type: "Data" top: "data" include { phase: TEST } transform_param { mean_file: "G:/interest_of_imags_for_recognation/VOC2012/Resize224/Img_val_mean.binaryproto" } data_param { source: "G:/interest_of_imags_for_recognation/VOC2012/Resize224/Img_val" batch_size: 1 backend: LMDB } } layer { name: "label" type: "Data" top: "label" include { phase: TEST } data_param { source: "G:/interest_of_imags_for_recognation/VOC2012/Resize224/Label_val" batch_size: 1 backend: LMDB } }.
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caffe-segnet has a low active ecosystem.
It has 13 star(s) with 6 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
There are 2 open issues and 2 have been closed. On average issues are closed in 3 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of caffe-segnet is current.
Quality
caffe-segnet has 0 bugs and 0 code smells.
Security
caffe-segnet has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
caffe-segnet code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
caffe-segnet has a Non-SPDX License.
Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
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caffe-segnet releases are not available. You will need to build from source code and install.
It has 5881 lines of code, 346 functions and 40 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of caffe-segnet
caffe-segnet Key Features
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caffe-segnet Examples and Code Snippets
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