semseg | Semantic Segmentation in Pytorch | Computer Vision library
kandi X-RAY | semseg Summary
kandi X-RAY | semseg Summary
This repository is a PyTorch implementation for semantic segmentation / scene parsing. The code is easy to use for training and testing on various datasets. The codebase mainly uses ResNet50/101/152 as backbone and can be easily adapted to other basic classification structures. Implemented networks including PSPNet and PSANet, which ranked 1st places in ImageNet Scene Parsing Challenge 2016 @ECCV16, LSUN Semantic Segmentation Challenge 2017 @CVPR17 and WAD Drivable Area Segmentation Challenge 2018 @CVPR18. Sample experimented datasets are ADE20K, PASCAL VOC 2012 and Cityscapes.
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Top functions reviewed by kandi - BETA
- Main worker function
- Train the model
- Get a logger
- Check if the worker is running
- Evaluate a single image
- Scale image
- Process the input image
- Color an image
- Check parameters
- Convenience function for forward computation
- Compute the PSA mask
- Merge a cfg from a list
- Coerce a value to a given type
- Attempt to decode a config value
- Argument parser
- Find a free port
semseg Key Features
semseg Examples and Code Snippets
Lines of Code : 8License : Permissive (MIT)
Copy# train on 4 GPUs
python -m torch.distributed.launch --nproc_per_node=4 train.py --config configs/cityscape_drn_c_26.json
# evaluate
python evaluate.py --logdir [run logdir] [-s]
# Moreover, you can add [your configs].json in run_tasks.sh
sh run_t @inproceedings{wang2019recurrent,
title={Recurrent U-Net for resource-constrained segmentation},
author={Wang, wei and Yu, Kaicheng and Hugonot, Joachim and Fua, Pascal and Salzmann, Mathieu},
booktitle={Proceedings of the IEEE International Co @InProceedings{,
author = {Lukas Liebel and Marco K\"orner},
title = {{MultiDepth}: Single-Image Depth Estimation via Multi-Task Regression and Classification},
booktitle = {IEEE Intelligent Transportation Systems Conference (ITSC)},
y Community Discussions
Trending Discussions on semseg
QUESTION
I am using a convolutional neural net (CNN) called make_unet from here. It works and code is able to run with this CNN. But I know that in deep learning you have to initialize weights for optimization of the neural network.
The documentation in Keras clearly indicates the use of a kernel_initializer for weight initialization. However, I do not see any kernel_initializer in the make_unet function I am using.
Anyone who can provide some insight would be appreciated.
...ANSWER
Answered 2018-Jan-26 at 07:42In Keras initialisers are passed on a per-layer basis via arguments kernel_initializer and bias_initializer, e.g.
QUESTION
I am trying to use the following CNN architecture for semantic pixel classification. The code I am using is here
However, from my understanding this type of semantic segmentation network typically should have a softmax output layer for producing the classification result.
I could not find softmax used anywhere within the script. Here is the paper I am reading on this segmentation architecture. From Figure 2, I am seeing softmax being used. Hence I would like to find out why this is missing in the script. Any insight is welcome.
...ANSWER
Answered 2019-Jan-08 at 06:20You are using quite a complex code to do the training/inference. But if you dig a little you'll see that the loss functions are implemented here and your model is actually trained using cross_entropy loss. Looking at the doc:
This criterion combines log_softmax and nll_loss in a single function.
For numerical stability it is better to "absorb" the softmax into the loss function and not to explicitly compute it by the model.
This is quite a common practice having the model outputs "raw" predictions (aka "logits") and then letting the loss (aka criterion) do the softmax internally.
If you really need the probabilities you can add a softmax on top when deploying your model.
QUESTION
I have an XML of the form:
ANSWER
Answered 2018-Sep-19 at 14:31This is working for me
/\*[local-name()='Envelope']/\*[local-name()='Triangle']/\*[local-name()='Triangle']/@time
QUESTION
I would like to follow the Convolutional Neural Net (CNN) approach here. However, this code in github uses Pytorch, whereas I am using Keras.
I want to reproduce boxes 6,7 and 8 where pre-trained weights from VGG-16 on ImageNet is downloaded and is used to make the CNN converge faster.
In particular, there is a portion (box 8) where weights are downloaded and skipped from VGG-16 that have no counterpart in SegNet (the CNN model). In my work, I am using a CNN model called U-Net instead of Segnet. The U-Net Keras code that I am using can be found here.
I am new to Keras and would appreciate any insight in Keras code on how I can go about downloading and skipping the VGG weights that have no counterpart with my U-Netmodel.
ANSWER
Answered 2018-Feb-12 at 15:10A solution sketch for this would look like the following:
Initialize VGG-16 and load the ImageNet weights by using the appropriate
weights='imagenet'flag:
QUESTION
I am new to Python and I am trying to use the function 'one_hot_to_label_batch' which can be found on line 115 from this website.
However, directly above this function there is a '@expand_dims'. This is the first time I have encountered this. I know 'expand_dims' is within Numpy but I do not know why it is defined as '@expand_dims' here. Any clarification would be appreciated.
ANSWER
Answered 2018-Jan-24 at 06:29This is a decorator. See the top of the file:
QUESTION
I am using code from this website
There is a function called 'label_to_one_hot_batch' within the file `isprs.py.
In my own python script in the main directory folder (i.e., the semseg folder), I am trying to import this function as follows:
ANSWER
Answered 2018-Jan-23 at 00:07There are probably many reasons, I know, about two.
One is when PYTHON_PATH is not properly specified and the root of the python project is not recognized.
The second is when a cyclic import occurs where the importing module ultimately imports certain module again.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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No vulnerabilities reported
Install semseg
You can use semseg 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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