Architecture | Focal Loss, FPN, ResNet |
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ID | 190397697 |
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Architecture | Focal Loss, FPN, ResNet |
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ID | 190397773 |
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Architecture | Focal Loss, FPN, ResNet |
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ID | 190397829 |
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RetinaNet is a one-stage object detection model that utilizes a focal loss function to address class imbalance during training. Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples. RetinaNet is a single, unified network composed of a backbone network and two task-specific subnetworks. The backbone is responsible for computing a convolutional feature map over an entire input image and is an off-the-self convolutional network. The first subnet performs convolutional object classification on the backbone's output; the second subnet performs convolutional bounding box regression. The two subnetworks feature a simple design that the authors propose specifically for one-stage, dense detection.
There are several RetinaNet 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/retinanet_R_101_FPN_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 | RetinaNet (R101, 3x) | box AP | 40.4 | # 70 |
COCO minival | RetinaNet (R50, 3x) | box AP | 38.7 | # 87 |
COCO minival | RetinaNet (R50, 1x) | box AP | 37.4 | # 100 |