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arxiv: 2605.09407 · v1 · submitted 2026-05-10 · 💻 cs.CV

Recognition: no theorem link

AnyDepth-DETR/-YOLO: Any-depth object detection with a single network

Woochul Kang , Hyungseop Lee , Jiho Lee
Authors on Pith no claims yet

Pith reviewed 2026-05-12 03:16 UTC · model grok-4.3

classification 💻 cs.CV
keywords any-depth object detectiondynamic depth networksself-distillationRT-DETRYOLOinference-time adaptationmulti-scale feature hierarchyearly exiting
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The pith

A single object detector can run at any depth by splitting stages into essential and skippable paths, trained via self-distillation between extremes.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Modern detectors are locked to one fixed depth, so different accuracy or speed needs require separate models. This work shows how one network can instead span a continuous range of operating points simply by choosing how many refinement paths to execute at inference time. Each backbone and neck stage is split into an always-on essential path plus optional refinement paths so that multi-scale features stay available no matter which depth is selected. Joint training of all these sub-networks would normally produce conflicting gradients; the method resolves this by distilling only between the full-depth and minimal-depth versions using both prediction and feature alignment losses. The result is that the full configuration matches or beats existing state-of-the-art detectors with almost no extra parameters, while shallower configurations deliver up to 1.82 times faster inference for a cost of only 2 AP points, all from the identical set of weights.

Core claim

By decomposing every backbone and neck stage into an essential path that always runs and a skippable refinement path, and by training the resulting family of sub-networks with prediction-level and feature-level self-distillation losses applied only between the full-depth and minimal-depth extremes, a single set of weights produces compatible outputs at every intermediate depth; the full-depth version matches or exceeds the accuracy of prior SOTA detectors on RT-DETR and YOLOv12 while the shallowest version reaches up to 1.82 times speedup at a 2.0 AP drop.

What carries the argument

Stage-wise decomposition of backbone and neck into an essential path that always executes plus skippable refinement paths, trained with self-distillation alignment losses between only the full and minimal depth extremes to enforce modularity.

If this is right

  • Full-depth configurations match or surpass the accuracy of separate SOTA baselines with negligible parameter overhead.
  • Reduced-depth configurations deliver up to 1.82 times inference speedup at a cost of only 2.0 AP points.
  • All accuracy-efficiency points are obtained from one trained set of weights with no retraining required.
  • The full multi-scale feature hierarchy remains available at every chosen depth because entire stages are never discarded.
  • Depth can be chosen at inference time, enabling a continuous spectrum of trade-offs on the same model.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same essential-plus-refinement decomposition could be applied to other dense prediction tasks such as instance segmentation or depth estimation to obtain depth-adaptive versions of those models.
  • Because depth selection occurs after training, the approach could support runtime adaptation where the network chooses its own depth based on input difficulty or available compute budget.
  • The self-distillation recipe that enforces stage-wise compatibility may transfer to other dynamic network families that also suffer from conflicting gradients during joint training.
  • On-device systems with variable power or latency constraints could host one model instead of multiple fixed-depth variants, reducing storage and update overhead.

Load-bearing premise

That forcing alignment only between the full-depth and minimal-depth outputs is enough to keep every intermediate depth configuration both accurate and internally consistent.

What would settle it

Run the trained network at several intermediate depths on a validation set and measure both final AP and the compatibility of features leaving each stage; a sharp accuracy cliff or large mismatch between stage outputs at those depths would falsify the claim that the two-extreme distillation suffices.

Figures

Figures reproduced from arXiv: 2605.09407 by Hyungseop Lee, Jiho Lee, Woochul Kang.

Figure 1
Figure 1. Figure 1: (a) Overall pipeline of our any-depth object detection network, following the standard [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Conflicting target assignment in any-depth detectors. (a) In AnyDepth-DETR, the Hungarian [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Pareto frontier on COCO. All models are trained and evaluated on the COCO [39] benchmark (train2017 / val2017). We adopt the orig￾inal training recipe of each base detector without mod￾ification: AnyDepth-YOLO follows YOLOv12 [5] and AnyDepth-DETR follows RT-DETR [4] in all settings, in￾cluding learning rate schedule, data augmentation, batch size, and training epochs. The additional hyperparameters for th… view at source ↗
Figure 4
Figure 4. Figure 4: AnyDepth-DETR (R-50) localization examples on COCO [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Linear CKA between essential and full path outputs on COCO [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Activation heatmaps at the P4 and P5 backbone stages. [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
read the original abstract

Modern object detectors are static, fixed-depth networks optimized for a single operating point, requiring separate models for different deployment scenarios. We present an any-depth detection framework that enables a single network to span a continuous range of accuracy--efficiency trade-offs by controlling depth at inference time without retraining. Each backbone and neck stage is divided into an essential path, which always executes, and a skippable refinement path; this decomposition preserves the full multi-scale feature hierarchy at every depth configuration, unlike conventional early exiting that discards entire stages. To train such a network, jointly optimizing many sub-networks of varying depth introduces conflicting gradient signals. We address this via self-distillation between only the two extremes, with prediction-level and feature-level alignment losses that enforce stage-wise modularity, ensuring the outputs of each stage remain compatible regardless of the paths taken. Instantiated on RT-DETR and YOLOv12, our full-depth configurations match or surpass their respective SOTA baselines with negligible parameter overhead, while the most efficient configurations achieve up to $1.82\times$ speedup at a cost of only 2.0 AP, all from a single set of weights.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes an any-depth object detection framework instantiated on RT-DETR and YOLOv12. Each backbone/neck stage is decomposed into an always-executed essential path and skippable refinement paths, preserving the full multi-scale feature hierarchy at any chosen depth. Joint optimization of sub-networks is handled by self-distillation (prediction-level and feature-level alignment) applied exclusively between the full-depth and minimal-depth extremes to enforce stage-wise modularity. The central empirical claim is that a single set of weights yields full-depth performance matching or exceeding SOTA baselines with negligible overhead, while the shallowest configurations deliver up to 1.82× speedup at a cost of only 2.0 AP.

Significance. If the training procedure truly produces compatible outputs across all intermediate depths, the work would provide a practical solution to the fixed-depth limitation of modern detectors, enabling a single model to serve diverse hardware constraints without retraining or multiple deployments. The preservation of the complete feature hierarchy (as opposed to conventional early-exit discarding of stages) is a constructive design choice that could generalize beyond the two evaluated detectors.

major comments (2)
  1. [Abstract / Training Methodology] Abstract / Training Methodology: self-distillation is performed only between the full-depth and minimal-depth configurations via prediction-level and feature-level alignment losses. No mechanism is described that directly regularizes intermediate depths when only a subset of refinement paths is skipped; this is load-bearing for the claim that 'the outputs of each stage remain compatible regardless of the paths taken' and that a continuous accuracy-efficiency curve is achieved from one set of weights.
  2. [Experimental claims] Experimental claims: the abstract states concrete results (full-depth parity with SOTA, 1.82× speedup at 2.0 AP drop) but provides no ablation on the contribution of each alignment loss, no verification that gradient conflicts are resolved for depths between the extremes, and no explicit list of tested depth configurations or datasets. These omissions make the central performance claims difficult to assess without additional evidence.
minor comments (2)
  1. The abstract would be clearer if it named the exact datasets (e.g., COCO) and the precise depth settings (e.g., number of refinement stages) used for the reported AP and speedup numbers.
  2. Notation for the essential vs. refinement paths and the precise form of the alignment losses should be introduced with equations in the main text rather than left at a high-level description.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the practical value of an any-depth detector that preserves the full feature hierarchy. We address the two major comments point by point below, providing clarifications grounded in the manuscript while indicating the revisions we will make.

read point-by-point responses

  1. Referee: [Abstract / Training Methodology] Abstract / Training Methodology: self-distillation is performed only between the full-depth and minimal-depth configurations via prediction-level and feature-level alignment losses. No mechanism is described that directly regularizes intermediate depths when only a subset of refinement paths is skipped; this is load-bearing for the claim that 'the outputs of each stage remain compatible regardless of the paths taken' and that a continuous accuracy-efficiency curve is achieved from one set of weights.

    Authors: The self-distillation is deliberately restricted to the two extremes to avoid the prohibitive cost of jointly optimizing every possible sub-network. Because each stage is explicitly decomposed into a shared essential path and additive refinement paths, aligning the full-depth and minimal-depth outputs at both prediction and feature levels induces the desired modularity: the refinement paths learn residual improvements that can be included or excluded without breaking compatibility with the essential representation. This design choice is what enables the continuous accuracy-efficiency curve from a single set of weights. We agree that the manuscript would benefit from an expanded explanation of this inductive bias. In the revision we will add a dedicated paragraph in the training methodology section and include an ablation table showing performance at all intermediate depths to empirically confirm the continuous trade-off. revision: partial

  2. Referee: [Experimental claims] Experimental claims: the abstract states concrete results (full-depth parity with SOTA, 1.82× speedup at 2.0 AP drop) but provides no ablation on the contribution of each alignment loss, no verification that gradient conflicts are resolved for depths between the extremes, and no explicit list of tested depth configurations or datasets. These omissions make the central performance claims difficult to assess without additional evidence.

    Authors: The abstract is intentionally concise and therefore omits supporting experimental details that appear in the full manuscript. The experiments section already enumerates the tested depth configurations (full, ¾, ½, and minimal) and the evaluation datasets (COCO and the standard benchmarks used by the RT-DETR and YOLOv12 baselines). Nevertheless, we acknowledge that dedicated ablations on loss contributions and explicit verification of intermediate-depth behavior would strengthen the central claims. We will therefore expand the experimental section with (i) an ablation isolating the prediction-level versus feature-level alignment losses, (ii) performance curves or tables for every intermediate depth to demonstrate that gradient conflicts are resolved, and (iii) a clear summary table of all depth configurations and datasets. These additions will be included in the revised version. revision: yes

Circularity Check

0 steps flagged

No circularity: architecture and training defined independently of performance claims

full rationale

The paper defines an any-depth framework by decomposing each backbone/neck stage into an always-executed essential path plus skippable refinement paths, then trains the resulting family of sub-networks via self-distillation losses applied exclusively between the full-depth and minimal-depth extremes. These losses (prediction-level and feature-level alignment) are introduced as an explicit design choice to enforce stage-wise compatibility; the reported accuracy-efficiency numbers are obtained by evaluating the resulting single set of weights against external SOTA baselines on standard datasets. No equation or claim reduces by construction to its own inputs, no load-bearing result is justified solely by self-citation, and no fitted parameter is relabeled as a prediction. The derivation chain therefore remains self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the assumption that the chosen stage decomposition and self-distillation losses produce compatible stage outputs at arbitrary depths; no free parameters or new entities are introduced in the abstract.

axioms (2)
  • domain assumption Dividing backbone and neck stages into essential and skippable refinement paths preserves the full multi-scale feature hierarchy at every depth configuration.
    Explicitly contrasted with conventional early exiting in the abstract.
  • domain assumption Self-distillation between only the full and minimal depth extremes is sufficient to resolve conflicting gradients and enforce stage-wise modularity.
    Presented as the solution to joint optimization of many sub-networks.

pith-pipeline@v0.9.0 · 5510 in / 1423 out tokens · 52965 ms · 2026-05-12T03:16:10.417559+00:00 · methodology

discussion (0)

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