Image_Segmentation | Pytorch implementation of U-Net , R2U-Net , Attention U | Model View Controller library

by LeeJunHyun Python Version: Current License: No License

X-Ray Key Features Code Snippets(2)Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | Image_Segmentation Summary

Image_Segmentation is a Python library typically used in Architecture, Model View Controller, Pytorch applications. Image_Segmentation has no bugs, it has no vulnerabilities and it has medium support. However Image_Segmentation build file is not available. You can download it from GitHub.

Pytorch implementation of U-Net, R2U-Net, Attention U-Net, and Attention R2U-Net.

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Security

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Image_Segmentation has a medium active ecosystem.

It has 2162 star(s) with 556 fork(s). There are 24 watchers for this library.

It had no major release in the last 6 months.

There are 45 open issues and 43 have been closed. On average issues are closed in 19 days. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Image_Segmentation is current.

Quality

Image_Segmentation has 0 bugs and 58 code smells.

Security

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

Image_Segmentation code analysis shows 0 unresolved vulnerabilities.

There are 15 security hotspots that need review.

License

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

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

Image_Segmentation saves you 334 person hours of effort in developing the same functionality from scratch.

It has 801 lines of code, 46 functions and 7 files.

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

Top functions reviewed by kandi - BETA

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

Train the model
Build the model
R Calculate sensitivity sensitivity
Calculate precision
Compute the sensitivity score
Compute Jaccard similarity
Calculate Density Coefficient
Returns the F1 correlation coefficient
Calculate the accuracy between two SDRs
Reset gradients to zero gradients
Print a progress bar
Creates a data loader
Remove directory if exists

Get all kandi verified functions for this library.

Image_Segmentation Key Features

No Key Features are available at this moment for Image_Segmentation.

Image_Segmentation Examples and Code Snippets

AIIA DNN Benchmark Overview,Evaluation & Results

Java

Lines of Code : 30

License : Permissive (Apache-2.0)

Copy

adb shell mkdir /sdcard/Android/data/com.xintongyuan.aibench/files
adb shell mkdir /sdcard/Android/data/com.xintongyuan.aibench/files/images
adb shell mkdir /sdcard/Android/data/com.xintongyuan.aibench/files/models
adb shell mkdir /sdcard/Android/dat

51WORLD虚拟标注数据集使用文档,4. 数据集目录结构说明

Python

Lines of Code : 27

License : Strong Copyleft (GPL-3.0)

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51Sim-One
    |--- train
        |--- scene1
            |---image_label 
            |---pcd_label 
            |---pcd_bin  
            |---image
            |---image_segmentation
            |---depth
            |---image_instance
            |

Community Discussions

Trending Discussions on Image_Segmentation

Use .tflite with iOS and GPU

Error incompatible shapes in function model.fit()

TensorFlow: Why is my Keras callback monitor value not available?

Pickle can't be load for Pascal VOC pickle dataset

QUESTION

Use .tflite with iOS and GPU

Asked 2020-May-10 at 10:55

I have created a new tflite model based on MobilenetV2. It works well without quantization using CPU on iOS. I should say that TensorFlow team did a great job, many thanks.

Unfortunately there is a problem with latency. I use iPhone5s to test my model, so I have the following results for CPU:

500ms for MobilenetV2 with 224*224 input image.
250-300ms for MobilenetV2 with 160*160 input image.

I used the following pod 'TensorFlowLite', '~> 1.13.1'

It's not enough, so I have read TF documentation related to optimization (post trainig quantization). I suppose I need to use Float16 or UInt8 quantization and GPU Delegate (see https://www.tensorflow.org/lite/performance/post_training_quantization). I used Tensorflow v2.1.0 to train and quantize my models.

Float16 quantization of weights (I used MobilenetV2 model after Float16 quantization)

https://github.com/tensorflow/examples/tree/master/lite/examples/image_segmentation/ios

pod 'TensorFlowLiteSwift', '0.0.1-nightly'

No errors, but model doesn’t work

pod 'TensorFlowLiteSwift', '2.1.0'

2020-05-01 21:36:13.578369+0300 TFL Segmentation[6367:330410] Initialized TensorFlow Lite runtime. 2020-05-01 21:36:20.877393+0300 TFL Segmentation[6367:330397] Execution of the command buffer was aborted due to an error during execution. Caused GPU Hang Error (IOAF code 3)

Full integer quantization of weights and activations

pod ‘TensorFlowLiteGpuExperimental’

Code sample: https://github.com/makeml-app/MakeML-Nails/tree/master/Segmentation%20Nails

I used a MobilenetV2 model after uint8 quantization.

...

ANSWER

Answered 2020-May-09 at 09:07

sorry for outdated documentation - the GPU delegate should be included in the TensorFlowLiteSwift 2.1.0. However, looks like you're using C API, so depending on TensorFlowLiteC would be sufficient.

MobileNetV2 do work with TFLite runtime in iOS, and if I recall correctly it doesn't have PAD op. Can you attach your model file? With the information provided it's a bit hard to see what's causing the error. As a sanity check, you can get quant/non-quant version of MobileNetV2 from here: https://www.tensorflow.org/lite/guide/hosted_models

For int8 quantized model - afaik GPU delegate only works for FP32 and (possibly) FP16 inputs.

Source https://stackoverflow.com/questions/61549368

QUESTION

Error incompatible shapes in function model.fit()

Asked 2019-Sep-11 at 15:40

I am new in Keras. I want to try U-net. I used this tutorial from tensorflow: https://github.com/tensorflow/models/blob/master/samples/outreach/blogs/segmentation_blogpost/image_segmentation.ipynb. I used the code for U-net creation with my own dataset. They have got images 256x256x3 and I made my images with same shape. Now, I got error:

...

ANSWER

Answered 2019-Sep-11 at 15:40

1376256 is exactly 3 x 458752. I suspect you are not correctly accounting for your channels somewhere. As this appears to be on your output layer, it may be that you're trying to predict 3 classes when there are only 1.

In future, or if this doesn't help, please provide more information including the code for your model and number of classes you're trying to predict, so people can better help.

Source https://stackoverflow.com/questions/57891174

QUESTION

TensorFlow: Why is my Keras callback monitor value not available?

Asked 2019-May-27 at 22:23

I use TensorFlow 1.12. I try to fit a model using Keras callbacks:

...

ANSWER

Answered 2019-May-27 at 22:23

Monitor 'val_loss' since your loss function is already set to your custom dice loss function.

The monitor parameter expects a metric. 'loss' is always available, and if you have validation data, so is 'val_loss'. Some folks like to use 'accuracy' and the validation therefor. If you had a custom metric function like sensitivity called (for example) sensitivity_deluxe(), you could include sensitivity_deluxe in the array of metrics in compile(), and it would be available to any callbacks referencing it in their monitor field. Any time you have validation data, you can prefix the metric string with 'val_'.

Example:

Source https://stackoverflow.com/questions/56326004

QUESTION

Pickle can't be load for Pascal VOC pickle dataset

Asked 2018-Feb-20 at 06:38

I'm trying to load Pascal VOC dataset from Stanford website here. Also trying to implement a code from Semantic Image Segmentation on Pascal VOC Pystruct blog. But I'm getting UnicodeDecodeError when I tried to load the pickle file. I tried below code so far:

...

ANSWER

Answered 2018-Feb-20 at 06:38

One of my friend told me the reason. Serialized object is a python2 object, so if you load with Python2, it's opening directly without any problem.

But If you would like to load with Python3, you need to add encoding parameters to pickle not into open function. Here is sample code:

Source https://stackoverflow.com/questions/48862141

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

Vulnerabilities

No vulnerabilities reported

Install Image_Segmentation

You can download it from GitHub.
You can use Image_Segmentation 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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