SegmentationCpp
segmentation_models
SegmentationCpp | segmentation_models | |
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3 | 8 | |
402 | 4,611 | |
- | - | |
3.4 | 0.0 | |
5 months ago | 4 months ago | |
C++ | Python | |
MIT License | MIT License |
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SegmentationCpp
- Show HN: A C++ Trainable DCNN Library for Semantic Segmentation
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Libtorch (C++ Front end for PyTorch)
Well, I just wrote a libtorch open source project here. In my experience, libtorch CUDA could be 2x or more faster than pytorch CUDA.
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C++ trainable semantic segmentation models
@misc{Chunyu:2021, Author = {Chunyu Dong}, Title = {Libtorch Segment}, Year = {2021}, Publisher = {GitHub}, Journal = {GitHub repository}, Howpublished = {\url{https://github.com/AllentDan/SegmentationCpp}} }
segmentation_models
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Instance segmentation of small objects in grainy drone imagery
Also, I’d suggest considering switching to the segmentation-models library - it provides U-Net models with a variety of pretrained backbones of as encoders. The author also put out a PyTorch version. https://github.com/qubvel/segmentation_models.pytorch https://github.com/qubvel/segmentation_models
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segmentation-models No module Error
I used segmentation-models (https://github.com/qubvel/segmentation_models) to create a deeplabv3+ model. I havent used it in the last 2 months and now i comeback to the same code and cant use it. Getting ModuleNotFoundError: No module named 'segmentation_models_pytorch.deeplabv3'
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recommendations for semantic segmentation of lowish volumes of biomedical images
I'm building some semantic segmentation models off of low-moderate volumes of biomedical images (~500 - 1k images). So far I've done some hyperparameter sweeping (learning rate, transfer learning, architectures, dropout layers) using the Segmentation Models package from qubvel https://github.com/qubvel/segmentation_models but I'm only seeing moderate performance and minimal differences between tested parameters.
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Can we use autoencoders to change an existing image instead of create one from scratch?
So, image segmentation (especially for satellite images) is a known problem. Search for semantic segmentation and unet (a model used for semantic segmentation). Also, if you use tensorflow there is this library which I found useful segmentation models.
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Anyone implemented latest image segmentation models/tuning from cvpr 2021?
I am doing an image segmentation project using https://github.com/qubvel/segmentation_models as the baseline. I was wondering if any of you have tried the latest segmentation models from cvpr papers. If yes, which ones you found to be interesting or actually improve miou. And how difficult/easy it is to implement those?
- Semantic Segmentation
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Any way to speed up inference prepare operations on host (CPU)?
That is just U-net from this repo, anything aside is slicing images to fit into window and predict call. I measure time of predict() and it is the same as profiler numbers, so definitely my other operations are beyond profiler. C API code is just creating tensors and calling TF_SessionRun plus slice operations with opencv. Can't post code, sorry.
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D Simple Questions Thread December 20 2020
I'm trying to train image segmentation model with transfer learning using https://github.com/qubvel/segmentation_models/.
What are some alternatives?
segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.
nnUNet
LibtorchTutorials - This is a code repository for pytorch c++ (or libtorch) tutorial.
efficientnet-lite-keras - Keras reimplementation of EfficientNet Lite.
tensorrtx - Implementation of popular deep learning networks with TensorRT network definition API
efficientnet - Implementation of EfficientNet model. Keras and TensorFlow Keras.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
BlenderProc - A procedural Blender pipeline for photorealistic training image generation
Drogon-torch-serve - Serve pytorch / torch models using Drogon
rembg-greenscreen - Rembg Video Virtual Green Screen Edition
torchRL - TorchRL is a C++ reinforcement library using PyTorch C++ backend LibTorch
unet - unet for image segmentation