RetinaNet
E431010
RetinaNet is a deep learning–based one-stage object detection model known for its focal loss function, which effectively addresses class imbalance to achieve high accuracy and speed.
All labels observed (1)
| Label | Occurrences |
|---|---|
| RetinaNet canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4326005 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RetinaNet Context triple: [torchvision, modelFamily, RetinaNet]
-
A.
DETR
DETR is the acronym for the former UK government Department of the Environment, Transport and the Regions, which was responsible for environmental policy, transport, and regional affairs.
-
B.
ResNet
ResNet is a deep convolutional neural network architecture known for its use of residual connections to enable very deep models and achieve state-of-the-art performance in image recognition tasks.
-
C.
YOLO
"YOLO" is a comedic hip-hop song and music video by The Lonely Island, featuring Adam Levine and Kendrick Lamar, that parodies the phrase "you only live once" by humorously promoting extreme caution and risk avoidance.
-
D.
ResNeXt
ResNeXt is a deep convolutional neural network architecture that extends ResNet by using grouped convolutions and a split-transform-merge strategy to improve accuracy and efficiency in image recognition tasks.
-
E.
Very Deep Convolutional Networks for Large-Scale Image Recognition
"Very Deep Convolutional Networks for Large-Scale Image Recognition" is the influential 2014 research paper that introduced the VGG family of deep convolutional neural network architectures, demonstrating that significantly increasing network depth with small convolutional filters leads to substantial improvements in image classification performance.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: RetinaNet Target entity description: RetinaNet is a deep learning–based one-stage object detection model known for its focal loss function, which effectively addresses class imbalance to achieve high accuracy and speed.
-
A.
DETR
DETR is the acronym for the former UK government Department of the Environment, Transport and the Regions, which was responsible for environmental policy, transport, and regional affairs.
-
B.
ResNet
ResNet is a deep convolutional neural network architecture known for its use of residual connections to enable very deep models and achieve state-of-the-art performance in image recognition tasks.
-
C.
YOLO
"YOLO" is a comedic hip-hop song and music video by The Lonely Island, featuring Adam Levine and Kendrick Lamar, that parodies the phrase "you only live once" by humorously promoting extreme caution and risk avoidance.
-
D.
ResNeXt
ResNeXt is a deep convolutional neural network architecture that extends ResNet by using grouped convolutions and a split-transform-merge strategy to improve accuracy and efficiency in image recognition tasks.
-
E.
Very Deep Convolutional Networks for Large-Scale Image Recognition
"Very Deep Convolutional Networks for Large-Scale Image Recognition" is the influential 2014 research paper that introduced the VGG family of deep convolutional neural network architectures, demonstrating that significantly increasing network depth with small convolutional filters leads to substantial improvements in image classification performance.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
convolutional neural network
ⓘ
deep learning model ⓘ object detection model ⓘ one-stage detector ⓘ |
| accuracyCharacteristic | high detection accuracy ⓘ |
| addressesProblem |
class imbalance in object detection
ⓘ
foreground-background class imbalance ⓘ |
| basedOn | cross-entropy loss ⓘ |
| category | computer vision model ⓘ |
| codeAvailability | open source implementations exist ⓘ |
| comparedTo | two-stage detectors ⓘ |
| designedFor | dense object detection ⓘ |
| detectionStageType | single-stage ⓘ |
| evaluatedOn | COCO dataset NERFINISHED ⓘ |
| field |
computer vision
ⓘ
deep learning ⓘ |
| hasArchitectureComponent |
box regression subnet
ⓘ
classification subnet ⓘ |
| hasAuthor |
Kaiming He
NERFINISHED
ⓘ
Piotr Dollár NERFINISHED ⓘ Priya Goyal NERFINISHED ⓘ Ross Girshick NERFINISHED ⓘ Tsung-Yi Lin NERFINISHED ⓘ |
| hasBackbone |
ResNet
NERFINISHED
ⓘ
ResNet-101 NERFINISHED ⓘ ResNet-50 NERFINISHED ⓘ |
| hasHyperparameter |
alpha in focal loss
ⓘ
gamma in focal loss ⓘ |
| hasKeyConcept | focal loss ⓘ |
| implementationFramework |
Caffe2
NERFINISHED
ⓘ
PyTorch NERFINISHED ⓘ TensorFlow NERFINISHED ⓘ |
| inputType | natural images ⓘ |
| introducedInPaper | Focal Loss for Dense Object Detection NERFINISHED ⓘ |
| lossFunctionProperty |
down-weights easy examples
ⓘ
focuses on hard, misclassified examples ⓘ |
| optimizationObjective | focal loss ⓘ |
| outperforms | many two-stage detectors on COCO ⓘ |
| outputType |
bounding boxes
ⓘ
class probabilities ⓘ |
| publicationYear | 2017 ⓘ |
| speedCharacteristic | high inference speed ⓘ |
| task | object detection in images ⓘ |
| trainingDataset | MS COCO NERFINISHED ⓘ |
| trainingType | supervised learning ⓘ |
| usesAnchorBoxes | true ⓘ |
| usesComponent |
FPN
NERFINISHED
ⓘ
Feature Pyramid Network NERFINISHED ⓘ |
| usesMultiScaleFeatures | true ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: RetinaNet Description of subject: RetinaNet is a deep learning–based one-stage object detection model known for its focal loss function, which effectively addresses class imbalance to achieve high accuracy and speed.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.
subject surface form:
torchvision