segmentation_models
cv-bird-segmentation
segmentation_models | cv-bird-segmentation | |
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8 | 1 | |
4,611 | 0 | |
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0.0 | 8.8 | |
4 months ago | about 2 months ago | |
Python | Jupyter Notebook | |
MIT License | GNU Affero General Public License v3.0 |
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segmentation_models
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Instance segmentation of small objects in grainy drone imagery
Also, Iād 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/.
cv-bird-segmentation
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Instance segmentation of small objects in grainy drone imagery
My question therefore is, how should I deal with drone imagery and small objects for instance segmentation tasks? What am I doing wrong and/or what should I be doing? Should I for example consider using an extra public dataset for bird/drone imagery segmentation first, before I fine-tune on my dataset? I am happy to provide more details if necessary. If useful, I uploaded my code here: https://github.com/augusts-bit/cv-animal-segmentation.
What are some alternatives?
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
coral-ordinal - Tensorflow Keras implementation of ordinal regression using consistent rank logits (CORAL) by Cao et al. (2019)
segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.
ModelZoo.pytorch - Hands on Imagenet training. Unofficial ModelZoo project on Pytorch. MobileNetV3 Top1 75.64š GhostNet1.3x 75.78š
pointnet - PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
MEAL-V2 - MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks. In NeurIPS 2020 workshop.