Detectron2

Detectron2 is Facebook AI Research's next generation library that provides state-of-the-art detection and segmentation a...

Detectron2

Detectron2 is Facebook AI Research's next generation library that provides state-of-the-art detection and segmentation algorithms. It is the successor of ... ,Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite ...

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Detectron2 相關參考資料
Detectron2 - Facebook AI

Detectron2 was built by Facebook AI Research (FAIR) to support rapid implementation and evaluation of novel computer vision research.

https://ai.facebook.com

Detectron2 is a platform for object detection ... - GitHub

Detectron2 is Facebook AI Research's next generation library that provides state-of-the-art detection and segmentation algorithms. It is the successor of ...

https://github.com

Detectron2 | Papers With Code

Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite ...

https://paperswithcode.com

detectron2MODEL_ZOO.md at main - GitHub

Detectron2 is a platform for object detection, segmentation and other visual recognition tasks. - detectron2/MODEL_ZOO.md at main ...

https://github.com

Facebook的Object Detection:Detectron 2 - CH.Tseng

2020年9月10日 — Detectron2環境需求如下:. Python >= 3.6; PyTorch >=1.3; OpenCV, needed by demo and visualization; pycocotools: ...

https://chtseng.wordpress.com

Installation — detectron2 0.6 documentation

python -m pip install 'git+https://github.com/facebookresearch/detectron2.git' # (add --user if you don't have permission) # Or, to install it from a local ...

https://detectron2.readthedocs

Python影像辨識筆記(二十):detectron2

detectron2是Facebook提供的Mask R-CNN的Python API,基於同樣屬於Facebook研發的PyTorch框架,使用起來相當方便。讓使用者可以在很短的時間內就完成Semantic ...

https://yanwei-liu.medium.com

利用detectron2快速使用faster RCNN - Medium

2020年1月17日 — 用faster RCNN檢測PCB板error. “利用detectron2快速使用faster RCNN” is published by Yuan Ko in Mess up.

https://medium.com