pytorch-semseg | Semantic Segmentation Architectures Implemented in PyTorch | Machine Learning library
kandi X-RAY | pytorch-semseg Summary
kandi X-RAY | pytorch-semseg Summary
Semantic Segmentation Architectures Implemented in PyTorch
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Top functions reviewed by kandi - BETA
- Load pretrained pretrained model .
- Train the model .
- Validate the trained model .
- Setup pre - encodings .
- Initialize the model .
- Get a model instance .
- Initialize VGG16 params .
- Resize image .
- Return a scheduler .
- Randomly crop the image .
pytorch-semseg Key Features
pytorch-semseg Examples and Code Snippets
# 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 pytorch-semseg
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.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install pytorch-semseg
You can use pytorch-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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