Mask_RCNN
mmsegmentation
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| Mask_RCNN | mmsegmentation | |
|---|---|---|
| 28 | 7 | |
| 24,119 | 7,380 | |
| 0.8% | 3.9% | |
| 0.0 | 8.6 | |
| 20 days ago | 10 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
Mask_RCNN
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Intuituvely Understanding Harris Corner Detector
The most widely used algorithms for classical feature detection today are "whatever opencv implements"
In terms of tech that's advancing at the moment? https://co-tracker.github.io/ if you want to track individual points, https://github.com/matterport/Mask_RCNN and its descendents if you want to detect, say, the cover of a book.
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Analyze defects and errors in the created images
Mask R-CNN
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List of AI-Models
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Thought Dump About Recent AI Advancements And Palantir
- Mask RCNN https://github.com/matterport/Mask_RCNN (open source, so also not Palantir's)
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Why are python dependencies so broken?
pip install git+https://github.com/matterport/Mask_RCNN
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DeepCreamPy & Hent-AI Guide: Installation and anime censorship removal (Version 2)
It is important to realize that to do its masking procedures, Hent-AI uses the Mask RCNN (MRCNN) package from Matterport. The problem with this version of MRCNN is that it is not compatible with Tensorflow 2.X versions, essentially limiting Hent-AI compatibility to strict Tensorflow 1.X versions. Since Tensorflow 1.15 is the last of the Tensorflow 1.X versions and uses CUDA 10.0, which supports a maximum compute capability of 7.5, this means that the last NVIDIA GPU series that is compatible with the original Hent-AI implementation is the RTX 2000 series. This is, of course, not optimal since it means that RTX 3000 series and later GPUs cannot be used despite their significant computing power and high VRAM.
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[P] Mask R-CNN (matterport) does not generate masks or just generates them randomly
I read that it could bethe problem with scipy version (https://github.com/matterport/Mask_RCNN/issues/2122) so I downgraded it, I also tried to modify shift = np.array([0, 0, 1., 1.]) in utils.py but nothing helped.
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Mask RCNN importing error
I am assuming you did a pip install of this github repository, or did you run pip install mrcnn. The mrcnn package on pypi is just an example package and doesn't have any useful functionality. In addition, where did you get the code from that you are trying to run, from someone else or did you write it yourself? Reason I am asking is because the import error is to be expected since there indeed is no InferenceConfig class defined in mrcnn.visualize.
- Maskrcnn - Mask r-cnn for object detection and segmentation
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MRCNN TF==2.7.0
Hello AI learners, check out my own development of Mask-RCNN supporting Tensorflow2.7.0 and Keras2.8.0. This is an edit of MRCNN which supports Tensoflow1.0, only.
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?
Swin-Transformer-Object-Detection - This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation.
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
yolact - A simple, fully convolutional model for real-time instance segmentation.
Swin-Transformer-Semantic-Segmentation - This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Semantic Segmentation.
mmdetection - OpenMMLab Detection Toolbox and Benchmark
segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.
Mask-RCNN-training-with-docker-containers-on-Sagemaker
face-parsing.PyTorch - Using modified BiSeNet for face parsing in PyTorch
Mask-RCNN-Implementation - Mask RCNN Implementation on Custom Data(Labelme)
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.
yolact - Tensorflow 2.x implementation YOLACT
labelme2coco - A lightweight package for converting your labelme annotations into COCO object detection format.