Fast R-CNN is an object detection model that improves in its predecessor R-CNN in a number of ways. Instead of extracting CNN features independently for each region of interest, Fast R-CNN aggregates them into a single forward pass over the image; i.e. regions of interest from the same image share computation and memory in the forward and backward passes.
There are several Fast R-CNN 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-Detection/fast_rcnn_R_50_FPN_1x.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}
}
Object Detection
| BENCHMARK | MODEL | METRIC NAME | METRIC VALUE | GLOBAL RANK |
|---|---|---|---|---|
| COCO minival | Fast R-CNN (Fast R-CNN R50-FPN, 1x) | box AP | 37.8 | # 96 |
