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Methods › Computer Vision › One-Stage Object Detection Models › RetinaNet

RetinaNet

209 papers tagged archive 2025-07-28

Introduced by Tsung-Yi Lin et al. in Focal Loss for Dense Object Detection

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

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-shelf 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.

We can see the motivation for focal loss by comparing with two-stage object detectors. Here class imbalance is addressed by a two-stage cascade and sampling heuristics. The proposal stage (e.g., Selective Search, EdgeBoxes, DeepMask, RPN) rapidly narrows down the number of candidate object locations to a small number (e.g., 1-2k), filtering out most background samples. In the second classification stage, sampling heuristics, such as a fixed foreground-to-background ratio, or online hard example mining (OHEM), are performed to maintain a manageable balance between foreground and background.

In contrast, a one-stage detector must process a much larger set of candidate object locations regularly sampled across an image. To tackle this, RetinaNet uses a focal loss function, a dynamically scaled cross entropy loss, where the scaling factor decays to zero as confidence in the correct class increases. Intuitively, this scaling factor can automatically down-weight the contribution of easy examples during training and rapidly focus the model on hard examples.

Formally, the Focal Loss adds a factor (1 - pₜ)^γ to the standard cross entropy criterion. Setting γ>0 reduces the relative loss for well-classified examples (pₜ>.5), putting more focus on hard, misclassified examples. Here there is tunable focusing parameter γ≥0.

FL(pₜ) = - (1 - pₜ)^γ log(pₜ)

PaperSourceSee Code · facebookresearch/Detectron

Papers archive 2025-07-28

30 shown of 209, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 153 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Object Detection163
object-detection152
Object96
Image Classification23
Semantic Segmentation19
Knowledge Distillation18
Instance Segmentation15
image-classification15
General Classification12
Pedestrian Detection11
Data Augmentation10
GPU9
Autonomous Driving8
Deep Learning8
Transfer Learning8
Classification7
Decoder7
Region Proposal7
Management6
Real-Time Object Detection6

Usage over time archive 2025-07-28

Papers per year tagged with RetinaNet: 2017 to 2025, peak 49 49 0 2017: 2 papers 2017 2018: 12 papers 2018 2019: 46 papers 2019 2020: 37 papers 2020 2021: 49 papers 2021 2022: 25 papers 2022 2023: 21 papers 2023 2024: 13 papers 2024 2025: 4 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (209 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

One-Stage Object Detection ModelsObject Detection Models

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