ETCI-2021-Competition-on-Flood-Detection
unet
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| ETCI-2021-Competition-on-Flood-Detection | unet | |
|---|---|---|
| 1 | 1 | |
| 150 | 4,435 | |
| - | - | |
| 1.8 | 0.0 | |
| almost 2 years ago | 19 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | MIT License |
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ETCI-2021-Competition-on-Flood-Detection
unet
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U-net Returning Black Square As Prediction
Issue with the model architecture and what I’m trying to do with it? I’ve used the model from this GitHub project (https://github.com/zhixuhao/unet), as it seemed somewhat similar to what I was trying to do. I understand what is happening from layer to layer, but not the input/output parts. (I’ll put the code for it below)
What are some alternatives?
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
Human-Segmentation-PyTorch - Human segmentation models, training/inference code, and trained weights, implemented in PyTorch
TTS - :robot: :speech_balloon: Deep learning for Text to Speech (Discussion forum: https://discourse.mozilla.org/c/tts)
segmentation_models - Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
Pseudo-Labelling - Pseudo Labelling on MNIST dataset in Tensorflow 2.x
cellpose - a generalist algorithm for cellular segmentation with human-in-the-loop capabilities
PySyft - Perform data science on data that remains in someone else's server
drrmsan - DRRMSAN: Deep Residual Regularized Multi-Scale Attention Networks for segmentation of medical images. Machine Leaning 2 (DA330) Course Project, RKMVERI.
Deep-Learning-In-Production - Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples.
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
BCDU-Net - BCDU-Net : Medical Image Segmentation