tpu
Mask_RCNN
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tpu | Mask_RCNN | |
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5 | 28 | |
5,179 | 24,146 | |
0.1% | 0.9% | |
6.3 | 0.0 | |
10 days ago | 25 days ago | |
Jupyter Notebook | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
tpu
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Variance in reported results on ImageNet between papers [D]
Found relevant code at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet + all code implementations here
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[D] What is the smallest, most capable, generative language model available now?
I'm looking for a generative-LM equivalent of an EfficientNet-Lite, for inference on devices with limited to no VRAM. I know about some popular ones like DistilGPT2. But it's been 2 years after its release. Surely, someone improved their size/performance ratio, right... right?
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Open source - that means free to use commercially right? ... right?
tensorflow/TPU 'apache' license - https://github.com/tensorflow/tpu/commit/6b3236d0271d2f2c3b2dfdc9d233ff00c4ba21cd
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[P] EfficientNet-lite in Keras (functional API).
According to original repository, the lite variants:
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Why do some architectures use no bias?
I was quite surprised to see that some architectures, like efficient net (official implementation: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py) don't use bias (bias=False). Does anyone know why is that? Apart from the obvious benefit of reducing parameters, doesn't it make the network less capable of learning certain representations?
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
Click to Learn more...
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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.
What are some alternatives?
mask-rcnn - Mask-RCNN training and prediction in MATLAB for Instance Segmentation
Swin-Transformer-Object-Detection - This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation.
efficientnet-lite-keras - Keras reimplementation of EfficientNet Lite.
yolact - A simple, fully convolutional model for real-time instance segmentation.
Deep-Residual-Learning-for-Image-Recognition - Implementation of https://arxiv.org/pdf/1512.03385.pdf
mmdetection - OpenMMLab Detection Toolbox and Benchmark
mmsegmentation - OpenMMLab Semantic Segmentation Toolbox and Benchmark.
Mask-RCNN-training-with-docker-containers-on-Sagemaker
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.