cnn-watermark-removal
Fully convolutional deep neural network to remove transparent overlays from images (by marcbelmont)
segmentation_models
Segmentation models with pretrained backbones. Keras and TensorFlow Keras. (by qubvel)
cnn-watermark-removal | segmentation_models | |
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1 | 8 | |
1,216 | 4,620 | |
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0.0 | 0.0 | |
about 3 years ago | 4 months ago | |
Python | Python | |
- | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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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.
cnn-watermark-removal
Posts with mentions or reviews of cnn-watermark-removal.
We have used some of these posts to build our list of alternatives
and similar projects.
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A minute of silence for this person please
There actually should be a way to remove static watermarks, at least there are AI libraries to to do so with still images, like https://github.com/marcbelmont/cnn-watermark-removal. The result will be mediocre, and it's gonna take enormous amount of time to clean up though.
segmentation_models
Posts with mentions or reviews of segmentation_models.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-09.
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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/.