caffe-tensorflow | Caffe models in TensorFlow | Machine Learning library

by ethereon Python Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | caffe-tensorflow Summary

caffe-tensorflow is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, OpenCV applications. caffe-tensorflow has no bugs, it has no vulnerabilities and it has medium support. However caffe-tensorflow build file is not available and it has a Non-SPDX License. You can download it from GitHub.

Caffe models in TensorFlow

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caffe-tensorflow has a medium active ecosystem.

It has 2835 star(s) with 1044 fork(s). There are 136 watchers for this library.

It had no major release in the last 6 months.

There are 105 open issues and 64 have been closed. On average issues are closed in 13 days. There are 14 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of caffe-tensorflow is current.

Quality

caffe-tensorflow has 0 bugs and 0 code smells.

Security

caffe-tensorflow has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

caffe-tensorflow code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

caffe-tensorflow 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.

Reuse

caffe-tensorflow releases are not available. You will need to build from source code and install.

caffe-tensorflow has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed caffe-tensorflow and discovered the below as its top functions. This is intended to give you an instant insight into caffe-tensorflow implemented functionality, and help decide if they suit your requirements.

Setup network
Convolutional convolutional layer
Average pooling op
Batch normalization
Set up the tensorflow
Load an image
Rescale image
Processes a single image
Import caffe
Map a convolution layer
Generate a batch of images
Map pooling
Return the representation of the node
Return a boolean mask for images
Returns the kernel parameters
Validate arguments
Map elementwise operation
Return the parameters of the layer
Load a model class by name
Convert data to TensorFlow
Setup the network
Validate the input image
Classify a single image
Loads the model
Setup the CNN
Decorator to add a layer to the layer

Get all kandi verified functions for this library.

caffe-tensorflow Key Features

No Key Features are available at this moment for caffe-tensorflow.

caffe-tensorflow Examples and Code Snippets

详细步骤解析：

C++

Lines of Code : 12

License : No License

Copy

model.meta
model.ckpt.data
model.ckpt.index

1-version folder
	model.pb(or model.pbtxt-humanable)
	variable--data folder
		variables.data-00000-of-00001
		variables.index

model.prototxt
model.caffemodel

model.param----op map table
model.bin----bina

NDDR-CNN, A Simple and General Architecture for General-Purpose CNN based Multi-Task Learning,Usage,Dataset

Python

Lines of Code : 5

License : No License

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datasets/nyu_v2/list
datasets/nyu_v2/nyu_train_val
models/vgg_deeplab_lfov
models/nyu_v2/slim_finetune_seg
models/nyu_v2/slim_finetune_normal

Ramwatcher

Python

Lines of Code : 5

License : Permissive (MIT)

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git clone --recursive https://github.com/xinshuoweng/ramwatcher

cd ramwatcher/xinshuo_toolbox
pip install -r requirements.txt

cd ..
python example.py

Community Discussions

Trending Discussions on caffe-tensorflow

Incomparable weight shape between caffe and tensorflow / keras

QUESTION

Incomparable weight shape between caffe and tensorflow / keras

Asked 2022-Feb-09 at 18:45

I am trying to convert a caffe model to keras, I have successfully been able to use both MMdnn and even caffe-tensorflow. The output I have are .npy files and .pb files. I have not had much luck with the .pb files, so I stuck to .npy files which contain the weights and biases. I have reconstructed an mAlexNet network as follows:

...

ANSWER

Answered 2022-Feb-09 at 18:45

The problem is the bias vector. It is shaped as a 4D tensor but Keras assumes it is a 1D tensor. Just flatten the bias vector:

Source https://stackoverflow.com/questions/71054511

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install caffe-tensorflow

You can download it from GitHub.
You can use caffe-tensorflow like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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