Image-Captioning | TensorFlow Implementation of Image Captioning | Machine Learning library

by zsdonghao Python Version: 1.0.1 License: No License

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

kandi X-RAY | Image-Captioning Summary

Image-Captioning is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. Image-Captioning has no bugs, it has no vulnerabilities and it has low support. However Image-Captioning build file is not available. You can download it from GitHub.

We reimplemented the complicated Google' Image Captioning model by simple TensorLayer APIs. This script run well under Python2 or 3 and TensorFlow 0.10 or 0.11.

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Quality

Security

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Image-Captioning has a low active ecosystem.

It has 106 star(s) with 55 fork(s). There are 6 watchers for this library.

It had no major release in the last 12 months.

There are 6 open issues and 1 have been closed. On average issues are closed in 270 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Image-Captioning is 1.0.1

Quality

Image-Captioning has 0 bugs and 0 code smells.

Security

Image-Captioning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Image-Captioning code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Image-Captioning 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-Captioning releases are available to install and integrate.

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

Image-Captioning saves you 3010 person hours of effort in developing the same functionality from scratch.

It has 6491 lines of code, 366 functions and 30 files.

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

Top functions reviewed by kandi - BETA

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

Fit a TensorFlow model
Convert a dict to one
Generate minibatches
Load a cifar10 dataset
Download and extract a file
Check if given path exists
Perform pretraining
Displays feature images
Inception V3
Compute the kernel size for a small input
Shift an image
Convert a text file to a list of token ids
Zoom data by zoom
Load and process the captions
Load a text8 dataset
Process images
Zoom the data in x
Rotate a vector
Create vocabulary
Load an IMDB dataset
Load a TBDB dataset
Load WMT data
Load MNIST dataset
Builds inputs
Test the network
Process image files

Get all kandi verified functions for this library.

Image-Captioning Key Features

No Key Features are available at this moment for Image-Captioning.

Image-Captioning Examples and Code Snippets

No Code Snippets are available at this moment for Image-Captioning.

Community Discussions

Trending Discussions on Image-Captioning

Error while using resnet50 model - project image captioning

from where to download resnet50.h5 file

How to concatenate two models in keras?

How can I generate validation data through a data generator when the model is trained through 'fit_generator' function?

AssertionError while trying to concatenate two models and fit in Keras

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

How to concatenate two models in keras?

Asked 2020-Aug-15 at 22:01

I wanted to use this model but we cannot use merge anymore.

...

ANSWER

Answered 2020-Aug-14 at 09:16

you should define the caption_in as 2D: Input(shape=(max_len,)). in your case, the concatenation must be operated on the last axis: axis=-1. the rest seems ok

Source https://stackoverflow.com/questions/63405139

QUESTION

How can I generate validation data through a data generator when the model is trained through 'fit_generator' function?

Asked 2020-Apr-07 at 14:41

I'm generating my image captioning model's training data through a data generator which is added below. This model is based on the model provided here. How can I generate and set validation data in a similar fashion during the training? I do have the features of the validation images and their captions.

Data Generator:

...

ANSWER

Answered 2020-Apr-07 at 14:41

You need another generator.

One for training, one for validation. Just create two generators, one using training data, the other using validation data.

Source https://stackoverflow.com/questions/61079662

QUESTION

AssertionError while trying to concatenate two models and fit in Keras

Asked 2018-Sep-29 at 10:42

I'm trying to develop an image captioning model. I'm referring to this Github repository. I have three methods, and they perform the following:

Generates the image model
Generates the caption model
Concatenates the image and caption model together

Since the code is long, I've created a Gist to show the methods.

Here is a summary of my image model and caption model.

But then I run the code, I am getting this error:

...

ANSWER

Answered 2018-Sep-29 at 10:40

You need the get the outputs of the models, using output attribute, and then use Keras functional API to be able to concatenate them (by either of Concatenate layer or its equivalent functional interface concatenate) and create the final model:

Source https://stackoverflow.com/questions/52567121

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

Vulnerabilities

No vulnerabilities reported

Install Image-Captioning

You can download it from GitHub.
You can use Image-Captioning 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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