Papers › TOOD: Task-aligned One-stage Object Detection

TOOD: Task-aligned One-stage Object Detection

17 Aug 2021ICCV 2021 10arXiv:2108.07755archive 2025-07-28

Chengjian Feng, Yujie Zhong, Yu Gao, Matthew R. Scott, Weilin Huang

One-stage object detection is commonly implemented by optimizing two sub-tasks: object classification and localization, using heads with two parallel branches, which might lead to a certain level of spatial misalignment in predictions between the two tasks. In this work, we propose a Task-aligned One-stage Object Detection (TOOD) that explicitly aligns the two tasks in a learning-based manner. First, we design a novel Task-aligned Head (T-Head) which offers a better balance between learning task-interactive and task-specific features, as well as a greater flexibility to learn the alignment via a task-aligned predictor. Second, we propose Task Alignment Learning (TAL) to explicitly pull closer (or even unify) the optimal anchors for the two tasks during training via a designed sample assignment scheme and a task-aligned loss. Extensive experiments are conducted on MS-COCO, where TOOD achieves a 51.1 AP at single-model single-scale testing. This surpasses the recent one-stage detectors by a large margin, such as ATSS (47.7 AP), GFL (48.2 AP), and PAA (49.0 AP), with fewer parameters and FLOPs. Qualitative results also demonstrate the effectiveness of TOOD for better aligning the tasks of object classification and localization. Code is available at https://github.com/fcjian/TOOD.

PaperPDFConference PDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2108.07755")

Code

Syntology Ran 2 of 4 code samples harvested from 2 repositories linked to this paper; 2 have no recorded run. Of those that ran: 1 ran · fixture could not drive it; 1 ran with no contract checked.

By repository: official repository: 2 samples from 1 repository, 1 ran; community (archive-listed): 2 samples from 1 repository, 1 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

fcjian/TOOD officialmentioned in papermentioned on GitHubpytorchApache-2.0 report
aakiraotok/yowov3 mentioned on GitHubpytorch report
astaxanthin/adasp mentioned on GitHubpytorch report
fcakyon/sahi-benchmark mentioned on GitHubpytorchMIT report
open-mmlab/mmdetection pytorchApache-2.0 report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

4 samples harvested; 2 ran; 0 honoured the contract we drafted; 2 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · fixture could not drive it
1ran
2unverified

Licence: 2 of the 4 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from 2 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

process_checkpoint fcjian/TOOD/.dev_scripts/gather_models.py official repository ran Apache-2.0 (permissive) · ae35528c6bd4bd1c · report
ordered_yaml_dump fcjian/TOOD/.dev_scripts/gather_models.py official repository unverified Apache-2.0 (permissive) · 8ff99a6bd2ebf598 · report
make_anchors aakiraotok/yowov3/utils/loss.py community (archive-listed) ran · fixture could not drive it no licence file found · pointer only · c6e98efc4b8343c0 · report
TAL aakiraotok/yowov3/utils/loss.py community (archive-listed) unverified no licence file found · pointer only · 5687b1d5756b4c88 · report

Tasks

2D Object DetectionObjectObject Detectionobject-detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
2D Object Detection CeyMo TOOD mAP 65.6 #3 of 5 Archive leaderboard report
Object Detection COCO test-dev TAL + TAP AP50 60.3 #173 of 225 Archive leaderboard report
Object Detection COCO test-dev TAL + TAP AP75 46.4 #173 of 225 Archive leaderboard report
Object Detection COCO test-dev TAL + TAP box mAP 42.5 #173 of 225 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

ATSSPAA

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections