Anime2Sketch
U-2-Net
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| Anime2Sketch | U-2-Net | |
|---|---|---|
| 7 | 30 | |
| 1,811 | 7,512 | |
| - | - | |
| 0.0 | 0.0 | |
| about 2 months ago | 9 months ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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Anime2Sketch
U-2-Net
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[Help] Meta's segment anything - How can I make smooth border ?
Hi :) I am app/web developer and new to AI. Currently, I am making photo app which can segment all the things in image. I've used meta's segment anything. I've got all the masks but the boundary of masks are very bumpy. So I've tried rembg which uses u2net(salient object detection) and pymatting together. Do I have to use pymatting separately after getting segment from segment anything to improve boundary quality of my segmented output ?
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BackgroundRemover 0.2.1 - Remove Background from Video and Images using AI
Cool, thanks for sharing. It might be worth clearly attributing the models you're using, and maybe add a models/license file with the U2net license, since that license is different to the one you're using for your project, and since you're distributing the models.
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How to do Human Head Segmentation from images?
Background Removal - I'd use u2net which has a model that's specifically trained on people vs backgrounds. If that didn't work, maybe DIS which is the newer version or rembg. These are pretty easy to get running I found.
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Just a reminder that there is a new 'remove background' extension for a1111
u2net_human_seg (download, source): A pre-trained model for human segmentation.
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OMPR V0.6.10 update
Optimized – AI tweak Image background remover is now faster and enables trained model (onnx) swapping Revamp the python engine for background remover. Should be running faster than the previous build. Also added was the ability to replace the pre-trained ONNX model by the user themselves. https://github.com/xuebinqin/U-2-Net
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Computer Vision Free Lancer
Also checkout https://github.com/xuebinqin/U-2-Net. They have a new version in this repo: https://github.com/xuebinqin/DIS
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image segmentation using U-nets
There, the author has the same goal as you do, and has a train.py and instructions. You can reach out to the author and ask questions either in the issues section or perhaps email directly. Many times people are very helpful when you show interest in their work. The neural network it is based on (U2-net) is very easy to get running by the way, and has lots of use cases: https://github.com/xuebinqin/U-2-Net
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After much experimentation 🤖
really any segmentation model could work. "salient object detection" is well suited for "i have a single, obvious subject that I want to isolate from the background". This is the model I had in mind, but it wouldn't have to be this necessarily: https://github.com/xuebinqin/U-2-Net
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[D] Extensions to U-nets
There's the U2 -net code paper. The paper and all the applications on the github are quite impressive. I've trained it on two different semantic segmentation tasks and found it performed well.
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Image to hand drawn
Sources: U2Net, ArtLine, Pix2PixHD, APDrawingGAN
What are some alternatives?
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
backgroundremover - Background Remover lets you Remove Background from images and video using AI with a simple command line interface that is free and open source.
image-background-remove-tool - ✂️ Automated high-quality background removal framework for an image using neural networks. ✂️
rembg-greenscreen - Rembg Video Virtual Green Screen Edition
trt_pose - Real-time pose estimation accelerated with NVIDIA TensorRT
UGATIT - Official Tensorflow implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation (ICLR 2020)
GANime - A deep learning program that automatically generates colorized anime characters based on sketch drawings.
ArtLine - A Deep Learning based project for creating line art portraits.
MiDaS - Code for robust monocular depth estimation described in "Ranftl et. al., Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer, TPAMI 2022"
awesome-semantic-segmentation - :metal: awesome-semantic-segmentation