Architecture | Panoptic FPN, Mask R-CNN, RoiAlign, ResNet |
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ID | 139514519 |
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Architecture | Panoptic FPN, Mask R-CNN, RoiAlign, ResNet |
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ID | 139514544 |
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Architecture | Panoptic FPN, Mask R-CNN, RoiAlign, ResNet |
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ID | 139514569 |
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Panoptic FPN endows Mask R-CNN, a popular instance segmentation method, with a semantic segmentation branch using a shared Feature Pyramid Network (FPN) backbone. This allows for the model to be applied to the panoptic segmentation task.
There are several Panoptic FPN models available in Detectron2, with different backbones and learning schedules.
To load from the Detectron2 model zoo:
from detectron2 import model_zoo
model = model_zoo.get("COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml", trained=True)
Replace the configuration path with the variant you want to use. You can find the paths in the model summaries at the top of this page.
You can follow the Getting Started guide on Colab to see how to train a model.
You can also read the official Detectron2 documentation.
@misc{wu2019detectron2,
author = {Yuxin Wu and Alexander Kirillov and Francisco Massa and
Wan-Yen Lo and Ross Girshick},
title = {Detectron2},
howpublished = {\url{https://github.com/facebookresearch/detectron2}},
year = {2019}
}
BENCHMARK | MODEL | METRIC NAME | METRIC VALUE | GLOBAL RANK |
---|---|---|---|---|
COCO minival | Panoptic FPN (R101-FPN, 3x) | PQ | 43.0 | # 1 |
boxAP | 42.4 | # 1 | ||
maskAP | 38.5 | # 1 | ||
COCO minival | Panoptic FPN (R50-FPN, 3x) | PQ | 41.5 | # 2 |
boxAP | 40.0 | # 2 | ||
maskAP | 36.5 | # 2 | ||
COCO minival | Panoptic FPN (R50-FPN, 1x) | PQ | 39.4 | # 3 |
boxAP | 37.6 | # 3 | ||
maskAP | 34.7 | # 3 |