image_segmentation | graph based image segmentation for better 2D screw hole

by meiqua C++ Version: Current License: No License

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kandi X-RAY | image_segmentation Summary

image_segmentation is a C++ library. image_segmentation has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

graph based image segmentation for better 2D screw hole recognition

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image_segmentation has a low active ecosystem.

It has 8 star(s) with 6 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

image_segmentation has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of image_segmentation is current.

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image_segmentation has no bugs reported.

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

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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

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Install image_segmentation

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