Mask_RCNN
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| Mask_RCNN | Mask-RCNN-training-with-docker-containers-on-Sagemaker | |
|---|---|---|
| 28 | 2 | |
| 24,096 | 5 | |
| 0.7% | - | |
| 0.0 | 0.0 | |
| 15 days ago | almost 3 years ago | |
| Python | Jupyter Notebook | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 only |
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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.
Mask-RCNN-training-with-docker-containers-on-Sagemaker
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Mask_RCNN adapted and trained on AWS sagemaker
The repo is almost like a guide to train whatever you want on sagemaker, with spot it can bring the cost down to less than 10$ for training. https://github.com/simone-viozzi/Mask-RCNN-training-with-docker-containers-on-Sagemaker
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.
yolact - A simple, fully convolutional model for real-time instance segmentation.
mmdetection - OpenMMLab Detection Toolbox and Benchmark
mmsegmentation - OpenMMLab Semantic Segmentation Toolbox and Benchmark.
Mask-RCNN-Implementation - Mask RCNN Implementation on Custom Data(Labelme)
yolact - Tensorflow 2.x implementation YOLACT
labelme - Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
SOLO - SOLO and SOLOv2 for instance segmentation, ECCV 2020 & NeurIPS 2020.
Moby - The Moby Project - a collaborative project for the container ecosystem to assemble container-based systems
labelme2coco - A lightweight package for converting your labelme annotations into COCO object detection format.
FreeCAD - This is the official source code of FreeCAD, a free and opensource multiplatform 3D parametric modeler.