segmentation_models | Segmentation models with pretrained backbones | Machine Learning library
kandi X-RAY | segmentation_models Summary
kandi X-RAY | segmentation_models Summary
Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
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Top functions reviewed by kandi - BETA
- Linknet layer
- Build Link Network
- Freeze model
- Filter kwargs from kwargs
- Create a PSNet
- Build a PSP layer
- Construct a spatial context block
- Decorator for convolutional convolutions
- Experiment V3 model
- 2D convolution layer
- Get preprocessing input
- Decorator to inject global submodules
- R Inception ResNet v2
- Resnet block
- Construct an FPN model
- Decorator for patches
- Calculate the crossentropy crossentropy
- Runs the project
- Creates a function that encodes a decamplingX2 block
- Calculate the focal loss
- Decorator for Conv1BlockTransposeX2 blocks
- Calculate recall
- Calculate the precision for each channel
- Calculate the iou - divergence score
- Set the framework
- Calculate f - score
- A unetet
segmentation_models Key Features
segmentation_models Examples and Code Snippets
Lines of Code : 26License : Permissive (MIT)
Copy [Available segmentation models]
Instance:
'maskrcnn'
Semantic:
'fpn', 'linknet', 'pspnet', 'unet'
[Available backbone architectures]
MobileNet:
'mobilenetv2', 'mobilenet'
DenseNet:
'densenet121', 'densenet169', 'densen 
License : No LicenseCopyconda env create -f environment.yml
conda create -n yourenvname python=3.6 anaconda
conda install -c anaconda tensorflow-gpu=2.0.0
conda install -c conda-forge keras
conda install -c conda-forge opencv
conda install -c conda-forge tqdm
Community Discussions
Trending Discussions on segmentation_models
QUESTION
I'm trying to implement the UNET at the keras website:
Image segmentation with a U-Net-like architecture
With only one change. use Dice loss instead of "sparse_categorical_crossentropy". However, every time I try something, I get different error. I'm coding on google colab using Tensorflow 2.7.
For example, I tried using
...ANSWER
Answered 2021-Dec-31 at 10:24You are passing 1-dimensional vectors to K.dot, while the ValueError is saying that K.dot requires arrays with 2-dimensions.
You can replace it with element-wise multiplication, i.e. intersection = K.sum(targets *inputs)
QUESTION
I want to use the Segmentation_Models UNet (with ResNet34 Backbone) for uncertainty estimation, so i want to add some Dropout Layers into the upsampling part. The Model is not Sequential, so i think i have to reconnect some outputs to the new Dropout Layers and the following layer inputs to the output of Dropout.
I'm not sure, whats the right way to do this. I'm currently trying this:
...ANSWER
Answered 2021-Oct-27 at 16:05There is a problem with the Resnet model you are using. It is complex and has Add and Concatenate layers (residual layers, I guess), which take as input a list of tensors from several "subnetworks". In other words, the network is not linear, so you can't walk through the model with a simple loop.
Regarding your error, in the loop of your code: layer is a layer and updated_model_layers is a tensor (functional API). Therefore, updated_model_layers.output does not exist. You confuse the two a bit
QUESTION
I have a U-Net model with pretrained weights from an Auto-encoder, The Auto-encoder was built an image dataset of 1400 images. I am trying to perform semantic segmentation with 1400 labelled images of a clinical dataset. The model performs well with an iou_score=0.97 on my test image dataset, but when I try to test it on a random image outside my dataset, I get a very bad segmentation result. I don't understand the reason for it. Please review my code and suggest me where I was wrong.
Training on my dataset & labels :
...ANSWER
Answered 2021-Oct-07 at 05:00Before training and validating you are normalizing data at this line -
QUESTION
I am trying to import segmentation models and keras and i am getting an attribute error, i am using tensor flow version 2.5.0
...ANSWER
Answered 2021-Jul-02 at 05:33I have solved my issue by adding tf.compat.v1.enable_eager_execution() to import and it works fine
QUESTION
A week ago, my Notebook in Google Colaboratory was working fine after installing the following libraries:
...ANSWER
Answered 2021-May-29 at 23:13You may need to install h5py of the following version, source.
QUESTION
I'm trying to use TensorFlow as backend yesterday I can use it, but today when I use it to show some error message when I'm trying to import Keras, so here's my code:
...ANSWER
Answered 2021-May-26 at 15:39Here is the solution to your problem, I've tested it on colab.
QUESTION
I am trying to use the Unet model output in other neural network, the problem is, I need to get the real shape without None instead of dimensions, could you please tell me how I can do it? unet_model.summary() shows the real shape, but when I try to get the output tensor, its shape is None
...ANSWER
Answered 2020-Dec-13 at 15:31You can get the layer shape of the model using below lines.
QUESTION
I want to use a pre-trained Unet model using segmentation_models API for the Cityscapes dataset, but I need the pre-trained weights for the same. Where can I find the pre-trained weights for a Unet model trained on the Cityscapes dataset?
Please guide me on this!!!
...ANSWER
Answered 2020-Dec-11 at 15:17UNet is absent from the benchmark so i assume it is not adapted for this dataset (too slow and not enough performant probably). However, I advise you to start with DeepLabv3+ from Google which is not so complicated and more adapted for this dataset.
You can use this repository where it is implemented, well documented and useable with pretrained weights from cityscape dataset (and also PascalVOC dataset).
QUESTION
from tensorflow import keras
from segmentation_models import PSPNet
ANSWER
Answered 2020-Dec-07 at 16:19You are facing this issue because you are using Tensorflow version >= 2.2. To fix this problem either you have to use Tensorflow 2.1/2.0 or Tensorflow 1.x (i.e 1.15.2)
Please follow below steps to perform Image segmentation using Segmentation models using TF 2.1.
QUESTION
I am trying to do semantic segmentation using Unet from segmentation model for multi channel (>3) image. The code works if the batch_size =1. But if I change the batch_size to other values (e.g. 2) then error occurs (InvalidArgumentError: Incompatible shapes):
...ANSWER
Answered 2020-Sep-23 at 10:59This error was solved by redefining a new image generator instead of simple_image_generator(). The simple_image_generator() worked well with the shape of the images (8 Bands) but did not cope well with the shape of the mask (1 band ).
During the execution, image_generator had 4 dimensions with [2,256,256,1] ( i.e. batch_size, (image size), bands) BUT mask_generator had 3 dimensions only vs. [2,256,256] (i.e. batch_size,(image size))
So reshaping the mask of [2,256,256] to [2,256,256, 1] solved the issue.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install segmentation_models
You can use segmentation_models 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.
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