segmentation_models.pytorch
json
segmentation_models.pytorch | json | |
---|---|---|
14 | 93 | |
8,844 | 40,332 | |
- | - | |
4.1 | 7.7 | |
8 days ago | 6 days ago | |
Python | C++ | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
segmentation_models.pytorch
-
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
-
[D] Improvements/alternatives to U-net for medical images segmentation?
SMP offers a wide variety of segmentation models with the option to use pre-trained weights.
-
Improvements/alternatives to U-net for medical images segmentation?
SMP has a lot of different choices for architecture other than unet, and a ton of different encoders. I like deeplabv3+/unet with regnety encoder, works well for most things https://github.com/qubvel/segmentation_models.pytorch
-
Medical Image Segmentation Human Retina
This basic example from segmentation models PyTorch repo would be good tutorial to start with. The library is very good, I like the unet, fpn and deeplabv3+ architectures with regnety as encoder https://github.com/qubvel/segmentation_models.pytorch/blob/master/examples/binary_segmentation_intro.ipynb
-
Automatic generation of image-segmentation mask pairs with StableDiffusion
Sounds like a good semantic segmentation problem, I like this repo: https://github.com/qubvel/segmentation_models.pytorch
-
Dice Score not decreasing when doing semantic segmentation
When i pass the CT-Scans and the masks to the Loss Function, which is the Jaccard-Loss from the segmentation_models.pytorch library, the value does not decrease but stay in the range of 1.0-0.9 over 50 epochs training on only one batch of 32 images. As far as I have understood, my network should overfit and the loss should decrease since I am only training on one batch of a small amount of images. However this does not happen. I also tried more batches with all the data over 100 epochs, but the loss does not decrease either obviously. Does anyone have an idea what I might have done wrong? Do I have to change anything when passing the masks to my loss function?
-
Good Brain Tumor segmentation model !?
I know there is a decent one in segmentation models python (MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation)
-
Advice needed
You could also use qubvel's segmentation models if you would like to explore semantic segmentation.
-
[D][R] Is there a standard architecture for U-Nets, pixel-to-pixel models, VAEs, and the like?
Check out segmentation models pytorch, really easy to use, has a great interface.
-
Pytorch GPU Memory Leak Problem: Cuda Out of Memory Error !!
Have you tried another implementation? For example: qubvel/segmentation_models.pytorch
json
-
Learn Modern C++
I have not done a "desktop" program in 25+ years and never using C++ (or C), since then I'm mostly a web developer (PHP,Elixir, JS, Kotlin etc).
I'm currently doing a C++ audio plugin with the Juce framework.
This website has been a good resource, alongside https://www.learncpp.com
But I was actually close to give up before using those two things:
- https://github.com/nlohmann/json : my plugin use a json api backend and the Juce json implementation is atrocious (apparently because of being born in previous c++ version), but this library is GREAT.
- ChatGPT 4. I'm not sure I would have "succeeded" without it, at least not in a reasonable time frame. ChatGPT 3.5 is slow and does not give good results for my use case but 4 is impressive. And I use in a very dumb way, just posing question in the web UI. I probably could have it directly in MSVC?
Also I must say, for all its flaws, I have a renewed appreciation for doing UI on the web ;)
- JSON for Modern C++ 3.11.3 (first release since 473 days)
-
What C++ library do you wish existed but hasn’t been created yet?
https://github.com/nlohmann/json works well for me
-
[CMake] Can't include external header in .h file
cmake_minimum_required(VERSION 3.15) project(xrpc++ DESCRIPTION "C++ AT Protocol XRPC library" VERSION 1.0.0 LANGUAGES CXX) include(FetchContent) FetchContent_Declare(cpr GIT_REPOSITORY https://github.com/libcpr/cpr.git GIT_TAG 2553fc41450301cd09a9271c8d2c3e0cf3546b73) # The commit hash for 1.10.x. Replace with the latest from: https://github.com/libcpr/cpr/releases FetchContent_MakeAvailable(cpr) FetchContent_Declare(json URL https://github.com/nlohmann/json/releases/download/v3.11.2/json.tar.xz) FetchContent_MakeAvailable(json) add_library(${PROJECT_NAME} SHARED src/lexicon.cpp src/xrpc.cpp ) target_link_libraries(${PROJECT_NAME} PRIVATE cpr::cpr) target_link_libraries(${PROJECT_NAME} PRIVATE nlohmann_json::nlohmann_json) set_target_properties(${PROJECT_NAME} PROPERTIES VERSION ${PROJECT_VERSION}) set_target_properties(${PROJECT_NAME} PROPERTIES SOVERSION 1) target_include_directories(${PROJECT_NAME} PUBLIC include) set(CMAKE_BUILD_TYPE debug)
FetchContent_Declare(json URL https://github.com/nlohmann/json/releases/download/v3.11.2/json.tar.xz) FetchContent_MakeAvailable(json)
-
It is either a clever technique or a sad failure
Here is one popular C++ library (nlohmann/json) removing its use.
-
How to compile project to separate files to prevent having single large executable as a result?
Before going into binary serialization I suggest you to get comfortable with serialization to text. You can try to write your data to text files and read them in again. Then after you get an idea of how this works you can try to use a library that writes to XML or json, e.g. nlohmann json
- What are some ways I can serialize objects?
-
C++ that allows tracking peer to peer multimedia streaming connections using a Flat File - NOT MySql
Download the single header file json.hpp from https://github.com/nlohmann/json/releases and place it in your project directory or an include directory.
-
C++ Reflection for Component Serialization and Inspection
Exemple of a JSON library: https://github.com/nlohmann/json (For XML, there's tinyxml)
What are some alternatives?
yolact - A simple, fully convolutional model for real-time instance segmentation.
RapidJSON - A fast JSON parser/generator for C++ with both SAX/DOM style API
mmsegmentation - OpenMMLab Semantic Segmentation Toolbox and Benchmark.
JsonCpp - A C++ library for interacting with JSON.
face-parsing.PyTorch - Using modified BiSeNet for face parsing in PyTorch
ArduinoJson - 📟 JSON library for Arduino and embedded C++. Simple and efficient.
EfficientNet-PyTorch - A PyTorch implementation of EfficientNet and EfficientNetV2 (coming soon!)
Boost.PropertyTree - Boost.org property_tree module
SegmentationCpp - A c++ trainable semantic segmentation library based on libtorch (pytorch c++). Backbone: VGG, ResNet, ResNext. Architecture: FPN, U-Net, PAN, LinkNet, PSPNet, DeepLab-V3, DeepLab-V3+ by now.
yaml-cpp - A YAML parser and emitter in C++
pyannote-audio - Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
cJSON - Ultralightweight JSON parser in ANSI C