pendulum_NN
segmentation_models
pendulum_NN | segmentation_models | |
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5 | 8 | |
9 | 4,617 | |
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0.0 | 0.0 | |
almost 3 years ago | 4 months ago | |
Python | Python | |
MIT License | MIT License |
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pendulum_NN
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I want to make an ai that plays games. I was wondering which programming language to use and resources to start from. Thanks in advance!
I coded a game environment and applied a genetic algorithm from scratch in python, here. Happy to answer any questions.
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What are the go-to algorithms for robot balancing?
If you really want a fun over-engineered solution, check out mine
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Where should I go next after having learned python and doing some basic projects.
I used Pygame and Keras to simulate and train an inverting pendulum
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Basic questions about neural net structure
My approach to an inverted pendulum game was evolving nets with genetic algorithms. The repo is decently commented if you want to take a look.
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Can you hlep me decide if a neural network would be the way to go?
A while back I evolved neural nets to balance an inverted pendulum. Designing an effective reward function and learning environment seemed to be the most critical aspects.
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?
stylegan2-projecting-images - Projecting images to latent space with StyleGAN2.
nnUNet
GnomansLand - An open-world reinforcement learning playground for gnomes filled with dangerous peril and bountiful treasure.
efficientnet-lite-keras - Keras reimplementation of EfficientNet Lite.
gym - A toolkit for developing and comparing reinforcement learning algorithms.
efficientnet - Implementation of EfficientNet model. Keras and TensorFlow Keras.
ml-agents - The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
BlenderProc - A procedural Blender pipeline for photorealistic training image generation
pytorch2keras - PyTorch to Keras model convertor
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.
rembg-greenscreen - Rembg Video Virtual Green Screen Edition
unet - unet for image segmentation