image_features | Extract deep learning features from images | Machine Learning library

by chsasank Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(5)Vulnerabilities Install Support

kandi X-RAY | image_features Summary

image_features is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, OpenCV applications. image_features has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Extract deep learning features from images using simple python interface

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Support

image_features has a low active ecosystem.

It has 86 star(s) with 18 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of image_features is current.

Quality

image_features has 0 bugs and 0 code smells.

Security

image_features has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

image_features code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

image_features does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

image_features releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

It has 231 lines of code, 16 functions and 9 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Compute features from given images
Get a pretrained model

Get all kandi verified functions for this library.

image_features Key Features

No Key Features are available at this moment for image_features.

image_features Examples and Code Snippets

No Code Snippets are available at this moment for image_features.

Community Discussions

Trending Discussions on image_features

TFX Tensorflow model validator component - You passed a data dictionary with keys ['image_raw_xf']. Expected the following keys: ['input_1']

Pytorch model training without using forward

Yocto do_package: Didn't find service unit specified in SYSTEMD_SERVICE_

TFRecord encoding nested objects

Yocto build error: Building image with debian package management

QUESTION

TFX Tensorflow model validator component - You passed a data dictionary with keys ['image_raw_xf']. Expected the following keys: ['input_1']

Asked 2021-Oct-20 at 02:20

I'm building a tfx pipeline based on the cifar10 example : [https://github.com/tensorflow/tfx/tree/master/tfx/examples/cifar10]

The difference is that I don't want to convert it to tf_lite model and instead use a regular keras based tensorflow model.

Everything works as expected until I get to the Evaluator component as it fails with the following error:

...

ANSWER

Answered 2021-Oct-20 at 02:20

Ok I found the answer. Because the model is expecting the input_1 name, then in _get_serve_image_fn, I need to create the dictionary key, such as:

Source https://stackoverflow.com/questions/69587252

QUESTION

Pytorch model training without using forward

Asked 2021-Apr-05 at 04:48

I'm working on training CLIP model. Here's the source code of the model https://github.com/openai/CLIP/blob/main/clip/model.py

Basically the CLIP object is constructed like this :

...

ANSWER

Answered 2021-Apr-05 at 04:48

The forward() in pytorch in nothing new. It just attaches the graph of your network when called. Backpropagation doesnt rely much on forward() because, the gradients are propagated through the graph.

The only difference is that in pytorch source, forward is similar to call() method with all the hooks registered in nn.Module.

Source https://stackoverflow.com/questions/66948189

QUESTION

Yocto do_package: Didn't find service unit specified in SYSTEMD_SERVICE_

Asked 2021-Feb-22 at 13:25

Description

I want to install a service into my image, but it is failing with following errors

...

ANSWER

Answered 2021-Feb-22 at 13:25

Replace system_unitdir by systemd_system_unitdir.

SYSTEMD_PACKAGES already contains ${PN} so you can ignore it, same for FILES_${PN} += "${systemd_system_unitdir}/mypackage.service" as if systemd.bbclass finds your unit, it'll be added to the appropriate FILES_ automatically.

c.f. https://git.yoctoproject.org/cgit/cgit.cgi/poky/tree/meta/classes/systemd.bbclass#n4 https://git.yoctoproject.org/cgit/cgit.cgi/poky/tree/meta/classes/systemd.bbclass#n109 https://git.yoctoproject.org/cgit/cgit.cgi/poky/tree/meta/classes/systemd.bbclass#n148

And for completeness, thanks to @Jussi Kukkonen for the comment, missing $ sign before {systemd_system_unitdir}.

Source https://stackoverflow.com/questions/66306192

QUESTION

TFRecord encoding nested objects

Asked 2020-Oct-07 at 02:02

I am new to Tensorflow and I am attempting to break up a large dataset into TFRecords. The format that I am encoding looks like this:

ID(String, bytes)
Index(int64)
Time (int64)
Image (Image, bytes)
Label (List of Label, bytes)

A Label object has FrameID(int64), Category(int64), x1(Float), x2(Float), y1(Float), y2(Float) However, I am struggling to get these information to be serialized. I broke up the List of Labels into Lists corresponding to their properties of the object (i.e, id[], category[] ...).

Currently, this is how individual elements are being serialized, adopted from TFRecord's document page:

...

ANSWER

Answered 2020-Oct-07 at 02:02

When created using _int64_list_feature / _float_list_feature Instead of FixedLenFeature([], tf.int64/tf.float32) try tf.io.VarLenFeature(tf.int64/tf.float32)

Source https://stackoverflow.com/questions/64229124

QUESTION

Yocto build error: Building image with debian package management

Asked 2020-Jan-29 at 13:43

I've build images with the default RPM package management, but now that I want to build an image with debian package management instead of RPM, Yocto returns an error in the last steps of image do_rootfs step.

The error;

...

ANSWER

Answered 2020-Jan-14 at 06:41

I figured out that one of the layers i was using, meta-linaro, has a .bbappend to busybox that configures dpkg-deb to be linked to busybox. I created a patch for the meta-linaro layer that removes that configuration and voilà, the build completes successfully.

Source https://stackoverflow.com/questions/59716949

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

Vulnerabilities

No vulnerabilities reported

Install image_features

You can download it from GitHub.
You can use image_features 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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