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Top 23 semantic-segmentation Open-Source Projects
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label-studio
Label Studio is a multi-type data labeling and annotation tool with standardized output format
14. LabelStudio by Human Signal | Github | tutorial
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InfluxDB
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Swin-Transformer
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
Project mention: Samsung expected to report 80% profit plunge as losses mount at chip business | news.ycombinator.com | 2023-10-10> there is really nothing that "normal" AI requires that is bound to CUDA. pyTorch and Tensorflow are backend agnostic (ideally...).
There are a lot of optimizations that CUDA has that are nowhere near supported in other software or even hardware. Custom cuda kernels also aren't as rare as one might think, they will often just be hidden unless you're looking at libraries. Our more well known example is going to be StyleGAN[0] but it isn't uncommon to see elsewhere, even in research code. Swin even has a cuda kernel[1]. Or find torch here[1] (which github reports that 4% of the code is cuda (and 42% C++ and 2% C)). These things are everywhere. I don't think pytorch and tensorflow could ever be agnostic, there will always be a difference just because you have to spend resources differently (developing kernels is time resource). We can draw evidence by looking at Intel MKL, which is still better than open source libraries and has been so for a long time.
I really do want AMD to compete in this space. I'd even love a third player like Intel. We really do need competition here, but it would be naive to think that there's going to be a quick catchup here. AMD has a lot of work to do and posting a few bounties and starting a company (idk, called "micro grad"?) isn't going to solve the problem anytime soon.
And fwiw, I'm willing to bet that most AI companies would rather run in house servers than from cloud service providers. The truth is that right now just publishing is extremely correlated to compute infrastructure (doesn't need to be but with all the noise we've just said "fuck the poor" because rejecting is easy) and anyone building products has costly infrastructure.
[0] https://github.com/NVlabs/stylegan2-ada-pytorch/blob/d72cc7d...
[1] https://github.com/microsoft/Swin-Transformer/blob/2cb103f2d...
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labelme
Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
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Project mention: Instance segmentation of small objects in grainy drone imagery | /r/computervision | 2023-12-09
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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Pytorch-UNet
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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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.
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semantic-segmentation-pytorch
Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset
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super-gradients
Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.
Most computer vision models are trained to predict on a preset list of label classes. In object detection, for instance, many of the most popular models like YOLOv8 and YOLO-NAS are pretrained with the classes from the MS COCO dataset. If you download the weights checkpoints for these models and run prediction on your dataset, you will generate object detection bounding boxes for the 80 COCO classes.
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Tools-to-Design-or-Visualize-Architecture-of-Neural-Network
Tools to Design or Visualize Architecture of Neural Network
Project mention: Programs to create model architectures schemes | /r/learnmachinelearning | 2023-05-31I can recommend this repo it offers a lot of visualization methods for neural networks.
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HRNet-Semantic-Segmentation
The OCR approach is rephrased as Segmentation Transformer: https://arxiv.org/abs/1909.11065. This is an official implementation of semantic segmentation for HRNet. https://arxiv.org/abs/1908.07919
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InternImage
[CVPR 2023 Highlight] InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
Project mention: What do you guys think of InternImage? - 65.5 mAP on COCO | /r/computervision | 2023-04-23 -
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Universal Data Tool
Collaborate & label any type of data, images, text, or documents, in an easy web interface or desktop app.
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Project mention: Looking for Point Cloud deep learning, training sources | /r/deeplearning | 2023-07-13
I already have a basic understanding with Open3D-ML and manage to get examples for training to work. However, my knowledge is not sufficient to transfer this to my own data or model deployment.
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pytorch-segmentation
:art: Semantic segmentation models, datasets and losses implemented in PyTorch.
So I have read the paper on segnet and understood its architechture, and how the corresponding model has been written on the segnet.py file. I have a dataset and segmentation masks (in PNG). I came across the code given in this repo: https://github.com/yassouali/pytorch-segmentation
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efficientdet-pytorch
A PyTorch impl of EfficientDet faithful to the original Google impl w/ ported weights
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involution
[CVPR 2021] Involution: Inverting the Inherence of Convolution for Visual Recognition, a brand new neural operator
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SaaSHub
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NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
The latest post mention was on 2024-03-13.
semantic-segmentation related posts
- OPENSCENE can identify objects, materials, affordances, activities, and room types in complex 3D scenes, all using a single model trained without any labeled 3D data
- What do you guys think of InternImage? - 65.5 mAP on COCO
- [D] What do you guys think of InternImage?
- [R] Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation
- Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
- Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows
- HFT: NEW Extended Research - star count:105.0
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A note from our sponsor - InfluxDB
www.influxdata.com | 18 Apr 2024
Index
What are some of the best open-source semantic-segmentation projects? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | label-studio | 16,385 |
| 2 | CVPR2024-Papers-with-Code | 15,923 |
| 3 | Swin-Transformer | 12,879 |
| 4 | labelme | 12,257 |
| 5 | awesome-semantic-segmentation | 10,220 |
| 6 | segmentation_models.pytorch | 8,773 |
| 7 | Pytorch-UNet | 8,315 |
| 8 | PaddleSeg | 8,202 |
| 9 | mmsegmentation | 7,342 |
| 10 | gluon-cv | 5,751 |
| 11 | semantic-segmentation-pytorch | 4,831 |
| 12 | super-gradients | 4,316 |
| 13 | Tools-to-Design-or-Visualize-Architecture-of-Neural-Network | 4,021 |
| 14 | awesome-data-labeling | 3,445 |
| 15 | HRNet-Semantic-Segmentation | 3,018 |
| 16 | InternImage | 2,286 |
| 17 | face-parsing.PyTorch | 2,073 |
| 18 | Universal Data Tool | 1,871 |
| 19 | Open3D-ML | 1,648 |
| 20 | pytorch-segmentation | 1,563 |
| 21 | efficientdet-pytorch | 1,549 |
| 22 | OneFormer | 1,329 |
| 23 | involution | 1,306 |
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