keras-resnet | : bullettrain_side : Residual wrapper for Keras | Computer Vision library

by codekansas Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | keras-resnet Summary

keras-resnet is a Python library typically used in Artificial Intelligence, Computer Vision, Tensorflow, Keras applications. keras-resnet has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However keras-resnet build file is not available. You can download it from GitHub.

:bullettrain_side: Residual wrapper for Keras

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Support

keras-resnet has a low active ecosystem.

It has 23 star(s) with 15 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 keras-resnet is current.

Quality

keras-resnet has 0 bugs and 0 code smells.

Security

keras-resnet has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

keras-resnet code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

keras-resnet is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

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

Builds the residual layer .
Saves a layer output .
Call the layer .
Initialize a residual from a given configuration .
Saves weights of a layer .
Get LSTM model .

Get all kandi verified functions for this library.

keras-resnet Key Features

No Key Features are available at this moment for keras-resnet.

keras-resnet Examples and Code Snippets

No Code Snippets are available at this moment for keras-resnet.

Community Discussions

Trending Discussions on keras-resnet

Error while using resnet50 model - project image captioning

from where to download resnet50.h5 file

Little to No GPU Usage during Custom Object Detection Training After Recent ImageAI Update

“TypeError: unhashable type: 'Dimension'” with BatchNormalization(axis=CHANNEL_AXIS)(input)

QUESTION

Error while using resnet50 model - project image captioning

Asked 2021-Mar-08 at 17:57

I have been trying to solve this error to complete my project but I dont get to know what I should do. Help me fixing this.

Code:

...

ANSWER

Answered 2021-Mar-08 at 05:32

resnet = ResNet50(include_top=False,weights='imagenet',input_shape=224,224,3),pooling='avg') 
resnet = load_model('resnet50_weights_tf_dim_ordering_tf_kernels.h5')
print("="*150) 
print("RESNET MODEL LOADED")

Source https://stackoverflow.com/questions/66513160

QUESTION

from where to download resnet50.h5 file

Asked 2021-Mar-05 at 20:44

I got the following error when trying to load a ResNet50 model. Where should I download the resnet50.h5 file?

...

ANSWER

Answered 2021-Mar-05 at 18:16

If you are looking for pre-trained weights of ResNet-50, you can find it here

Source https://stackoverflow.com/questions/66496891

QUESTION

Little to No GPU Usage during Custom Object Detection Training After Recent ImageAI Update

Asked 2021-Jan-10 at 14:49

I have trained a custom object detection model using 750 images using ImageAI on Google Colab Pro about a month ago using TensorFlowGPU 1.13 and have roughly 30min/epoch training time. Now, when I train using the same dataset but with TensorFlowGPU 2.4.3 (ImageAI doesnt support old TF anymore), I am getting very little GPU usage (0.1GB) and 6 hour per epoch training times. I have tried training the same model on my local machine and I am getting very slow training times as well.

I am using the following imports (based on ImageAI documentation):

!pip install tensorflow-gpu==2.4.0 keras==2.4.3 numpy==1.19.3 pillow==7.0.0 scipy==1.4.1 h5py==2.10.0 matplotlib==3.3.2 opencv-python keras-resnet==0.2.0 !pip install imageai --upgrade

I am pulling my training data from Google Drive.

Is there anything I could be missing that could speed up my object detection training times on either Google Colab or my local machine? The slow training times is slowing my research down.

...

ANSWER

Answered 2021-Jan-10 at 14:49

If you want full GPU usage, from my experience, you must revert back to previous versions of ImageAI and it's compatible packages. Here is a list of compatible packages that I have installed that work as of now (January 2021) on my local machine and Google Colab:

TF-GPU==1.13.1
Keras==2.2.4
Imageai==2.1.0

This fixed any issue caused by the most recent patch of ImageAI. I now am back to full GPU usage. Until the issue is patched, I suggest using the old version.

Source https://stackoverflow.com/questions/65632510

QUESTION

“TypeError: unhashable type: 'Dimension'” with BatchNormalization(axis=CHANNEL_AXIS)(input)

Asked 2020-Sep-14 at 10:57

I have the following ResNet 3D architecture that I got from github. It is the Keras implementation of R3D. This architecture is intended to train models on video classification

...

ANSWER

Answered 2020-Sep-14 at 10:57

To solve the problem, we need to cast every shape access to int.

Example : residual.shape[CHANNEL_AXIS] needs to be rewritten int(residual.shape[CHANNEL_AXIS])

The new version of the code is as follows :

Source https://stackoverflow.com/questions/63845434

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

Vulnerabilities

No vulnerabilities reported

Install keras-resnet

You can download it from GitHub.
You can use keras-resnet 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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