river-runner
segmentation_models.pytorch
Our great sponsors
river-runner | segmentation_models.pytorch | |
---|---|---|
71 | 14 | |
375 | 8,773 | |
- | - | |
7.9 | 2.8 | |
6 days ago | 9 days ago | |
Svelte | Python | |
GNU General Public License v3.0 only | 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.
river-runner
- Drop a raindrop anywhere in the world and follow its course to its course to the ocean
- Drop a raindrop anywhere in the world and follow its course to the ocean
- Heavily contaminated water in East Palestine, Ohio.
- Is there an interactive river map where you can touch a waterway, and its route downstream is highlighted?
-
Viewing the dead sperm whale
River Runner
- Follow the path of a drop of water from anywhere in the world: River Runner Global
- River Runner Global - Click on any spot on Earth and see where a raindrop there will flow until it reaches an ocean.
- River Runner Global - Tap to drop a raindrop anywhere in the world and watch where it ends up
- Where is all the water going?
-
Collection of really good websites
Amazing articles -- http://worrydream.com/ how technology works -- https://ciechanow.ski/ Explore scale of the Universe -- https://joshworth.com/dev/pixelspace/pixelspace_solarsystem.html Find best sites on Internet -- https://cloudhiker.net/ Click on map to trace a drop of rain and where it will go to -- https://river-runner-global.samlearner.com/ Track wind movement around the world -- https://www.windy.com/?27.714,85.314,4 Website of Nintendo people -- https://y-n10.com/ Everything about shoelaces -- https://www.fieggen.com/shoelace/ See satellite that is going to come near you -- https://james.darpinian.com/satellites/#
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
What are some alternatives?
mkdocs-material - Documentation that simply works
mmsegmentation - OpenMMLab Semantic Segmentation Toolbox and Benchmark.
riju - ⚡ Extremely fast online playground for every programming language.
yolact - A simple, fully convolutional model for real-time instance segmentation.
OpenHD - OpenHD
face-parsing.PyTorch - Using modified BiSeNet for face parsing in PyTorch
svelte-mapbox - MapBox Map and Autocomplete components for Svelte (or Vanilla JS)
EfficientNet-PyTorch - A PyTorch implementation of EfficientNet and EfficientNetV2 (coming soon!)
SICL - A fresh implementation of Common Lisp
pyannote-audio - Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
react-native-everywhere
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.