Mask-RCNN-Implementation VS taco

Compare Mask-RCNN-Implementation vs taco and see what are their differences.

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Mask-RCNN-Implementation taco
1 2
3 1,211
- 1.3%
3.2 0.0
about 3 years ago 24 days ago
Jupyter Notebook C++
MIT License GNU General Public License v3.0 or later
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Mask-RCNN-Implementation

Posts with mentions or reviews of Mask-RCNN-Implementation. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-04-09.

taco

Posts with mentions or reviews of taco. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-06-15.
  • The Distributed Tensor Algebra Compiler (2022)
    4 projects | news.ycombinator.com | 15 Jun 2023
    I agree! Much of this work was done as part of the overarching TACO project (https://github.com/tensor-compiler/taco), in an attempt to distribute sparse tensor computations (https://rohany.github.io/publications/sc2022-spdistal.pdf). MLIR recently (~mid 2022) began implementing the ideas from TACO into a "sparse tensor" dialect, so perhaps some of these ideas could make it into there. I'm working with MLIR these days, and if I could re-do the project now I would probably utilize and targetb the MLIR linalg infrastructure!
  • Qué tire la primer piedra, aquien no le ha pasado así....?
    1 project | /r/mexico | 14 Jul 2022

What are some alternatives?

When comparing Mask-RCNN-Implementation and taco you can also consider the following projects:

labelme - Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).

blitz - Blitz++ Multi-Dimensional Array Library for C++

Mask_RCNN - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

Grassmann.jl - ⟨Grassmann-Clifford-Hodge⟩ multilinear differential geometric algebra

Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.

CuTeLib - CUDA Template Library provides simple, typesafe, performant constructs for C++ CUDA projects

Face-Mask-Detection - Face Mask Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras

MegEngine - MegEngine 是一个快速、可拓展、易于使用且支持自动求导的深度学习框架

TACO - 🌮 Trash Annotations in Context Dataset Toolkit

YOLOX - YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/

ktrain - ktrain is a Python library that makes deep learning and AI more accessible and easier to apply

theme-ui - Build consistent, themeable React apps based on constraint-based design principles