NeuralNetworks
segmentation_models
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NeuralNetworks | segmentation_models | |
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4 | 8 | |
65 | 4,602 | |
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0.0 | 0.0 | |
over 1 year ago | 4 months ago | |
Python | Python | |
MIT License | MIT License |
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NeuralNetworks
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I created a video about how you can train a neural network (in python) to learn complex image/video classification tasks (like in-game detection) using transfer learning! The GitHub repo is linked in the video description. Hope this is useful or helpful for some of you guys :-)
Here’s the GitHub link for anyone else like me who didn’t want to have to go to YouTube to get it.
- [P][Code Release] A neural network implementation to detect whether a given video clip is in-game or not using transfer learning (can be applied to all sorts of games)
- We created a neural network implementation to detect whether a given video clip is in-game or not using transfer learning (can be applied to all sorts of games)
- [Code Release] A neural network implementation to detect whether a given video clip is in-game or not using transfer learning (can be applied to all sorts of games)
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?
coral-ordinal - Tensorflow Keras implementation of ordinal regression using consistent rank logits (CORAL) by Cao et al. (2019)
nnUNet
efficientnet-lite-keras - Keras reimplementation of EfficientNet Lite.
efficientnet - Implementation of EfficientNet model. Keras and TensorFlow Keras.
BlenderProc - A procedural Blender pipeline for photorealistic training image generation
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
segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.
ModelZoo.pytorch - Hands on Imagenet training. Unofficial ModelZoo project on Pytorch. MobileNetV3 Top1 75.64🌟 GhostNet1.3x 75.78🌟
pointnet - PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation