Project AURIONAI Use-Cases Research & Innovation
University of Adelaide
vision

FCOS

Anchor-free one-stage detector, from the paper "FCOS: Fully Convolutional One-Stage Object Detection".

Figure from the FCOS paper: As shown in the left image, FCOS works by predicting a 4D vector (l,t,r,b)(l,t,r,b) encoding the location of a bounding box at each foreground pixel.
Levelmodel
Classvision
Parametersnot in the notes
Contextnot in the notes
Released2 Apr 2019
Opennessunknown
Runs onnot in the notes
Statusactive

R&D Topics

Identity

Developer
University of Adelaide
Family
not in the notes
Released
2 Apr 2019 sourcesannouncement page
Weights published
–
Record checked
12 Oct 2026
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.

Architecture and Size

Architecture
CNN
Total parameters
– sources
Active parameters
–
Checkpoint parameters
–
Context length
not in the notes
Input
image
Output
labels and boxes
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).

Openness and Access

Openness
unknown
Licence
not in the notes sources
Access routes
open weights download
Where this comes from
The licence file governs over registry tags and aggregator labels.

Deployment

Runs on
not in the notes
Footprint
not in the notes
Runtimes
not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.

Indonesia

Indonesian support
not stated
Regional languages
not in the notes
Jakarta hosting
unknown
Languages
not in the notes
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.

Benchmark Results

The notes record no benchmark result for this model.

Lineage

The notes record no lineage for this model.

Industry Use Cases

No industry use case links this model.

Sources

  1. arxiv.org/abs/1904.01355 https://arxiv.org/abs/1904.01355
  2. paperswithcode.com/method/fcos https://paperswithcode.com/method/fcos
  3. huggingface.co/datasets/pwc-archive/methods https://huggingface.co/datasets/pwc-archive/methods
  4. arxiv.org/abs/1904.01355v5 https://arxiv.org/abs/1904.01355v5
  5. github.com/open-mmlab/mmdetection/tree/main/configs/fcos https://github.com/open-mmlab/mmdetection/tree/main/configs/fcos
  6. github.com/aim-uofa/adet https://github.com/aim-uofa/adet
  7. arxiv.org/abs/2001.00309 https://arxiv.org/abs/2001.00309
  8. arxiv.org/html/1904.01355 https://arxiv.org/html/1904.01355

To cite this page: Project AURION, AI Use-Cases Research & Innovation, "FCOS", https://aurion.semai.id/models/fcos/, records of October 2026.