Training Techniques | Weight Decay, SGD with Momentum, Focal Loss |
---|---|
Architecture | FPN, 1x1 Convolution, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax, Non Maximum Suppression |
ID | retinanet_resnet50_fpn |
SHOW MORE |
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.
To load a pretrained model:
import torchvision.models as models
retinanet_resnet50_fpn = models.detection.retinanet_resnet50_fpn(pretrained=True)
Replace the model name with the variant you want to use, e.g. retinanet_resnet50_fpn
. You can find
the IDs in the model summaries at the top of this page.
To evaluate the model, use the object detection recipes from the library.
You can follow the torchvision recipe on GitHub for training a new model afresh.
@article{DBLP:journals/corr/abs-1708-02002,
author = {Tsung{-}Yi Lin and
Priya Goyal and
Ross B. Girshick and
Kaiming He and
Piotr Doll{\'{a}}r},
title = {Focal Loss for Dense Object Detection},
journal = {CoRR},
volume = {abs/1708.02002},
year = {2017},
url = {http://arxiv.org/abs/1708.02002},
archivePrefix = {arXiv},
eprint = {1708.02002},
timestamp = {Mon, 13 Aug 2018 16:46:12 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1708-02002.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
BENCHMARK | MODEL | METRIC NAME | METRIC VALUE | GLOBAL RANK |
---|---|---|---|---|
COCO minival | RetinaNet ResNet-50 FPN | box AP | 36.4 | # 108 |