image-background-remove-tool
mmsegmentation
| image-background-remove-tool | mmsegmentation | |
|---|---|---|
| 16 | 7 | |
| 1,237 | 7,414 | |
| - | 1.8% | |
| 4.3 | 8.2 | |
| 5 days ago | 5 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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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.
image-background-remove-tool
- Show HN: Image background removal without annoying subscriptions
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Python: Please stop screwing over Linux distros
It was a little bit ago, but I believe I wanted to mess around with this project https://github.com/OPHoperHPO/image-background-remove-tool. There is a requirement.txt but pip3 wouldn't install it. On arch linux, no reason it shouldn't work just ran into dependency issues.
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Creating an Image Background Removal API (Remove.bg clone) using ⚡Azure Functions⚡
I was looking for a API with which you can remove the background of an image. I found a few and saw that they were pretty expensive. I googled some more and found this repository https://github.com/OPHoperHPO/image-background-remove-tool which does the job pretty well.
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Google Colab Not using GPU Properly
I would guess this is not a Colab-specific problem, as it appears this issue has been addressed in Github multiple times, inside and outside of Colab.
- Google Colab is Kicking my A$$
- File Not Found Error
mmsegmentation
- [D] The MMSegmentation library from OpenMMLab appears to return the wrong results when computing basic image segmentation metrics such as the Jaccard index (IoU - intersection-over-union). It appears to compute recall (sensitivity) instead of IoU, which artificially inflates the performance metrics.
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Is there any ML model out there for room surfaces detection? (ceiling, floor, windows)
Segmentation models trained on datasets like ADE20k could probably be used for that, because it has separate classes for these things iirc. https://github.com/open-mmlab/mmsegmentation should have suitable pretrained models available.
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MMDeploy: Deploy All the Algorithms of OpenMMLab
MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
- Mmsegmentation - Openmmlab semantic segmentation toolbox and benchmark.
- Mmsegmentation – Openmmlab semantic segmentation toolbox and benchmark
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Semantic Segmentation models
This repo is amazing: https://github.com/open-mmlab/mmsegmentation
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What's A Simple Custom Segmentation Pipeline?
Mmsegmentation would be a good place to start for basic segmentation. They have lots of recent methods and pretained models you could fine-tune from. They also support quite a few datasets including VOC. There is a custom dataset format which looks straightforward to create.
What are some alternatives?
U-2-Net - The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
TorchGA - Train PyTorch Models using the Genetic Algorithm with PyGAD
Swin-Transformer-Semantic-Segmentation - This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Semantic Segmentation.
QualityScaler - QualityScaler - image/video deeplearning upscaling for any GPU
segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.
transparent-background - This is a background removing tool powered by InSPyReNet (ACCV 2022)
Mask_RCNN - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
Visual Studio Code - Visual Studio Code
face-parsing.PyTorch - Using modified BiSeNet for face parsing in PyTorch
sd-webui-segment-anything - Segment Anything for Stable Diffusion WebUI
PaddleSeg - Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc.