OD$^3$: Optimization-free Dataset Distillation for Object Detection
Authors: Salwa K. Al Khatib
Ahmed Elhagry
Shitong Shao
Zhiqiang Shen
Venue: ICLR 2026 Poster (2026)
Keywords: dataset distillation
object detection
data-centric framework
efficient machine learning
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We introduce OD³, a novel optimization-free data distillation framework specifically designed for object detection.

Abstract: Training large neural networks on large-scale datasets requires substantial computational resources, particularly for dense prediction tasks such as object detection. Although dataset distillation (DD) has been proposed to alleviate these demands by synthesizing compact datasets from larger ones, most existing work focuses solely on image classification, leaving the more complex detection setting largely unexplored. In this paper, we introduce OD$^3$, a novel optimization-free data distillation framework specifically designed for object detection. Our approach involves two stages: first, a candidate selection process in which object instances are iteratively placed in synthesized images based on their suitable locations, and second, a candidate screening process using a pre-trained observer model to remove low-confidence objects. We perform our data synthesis framework on MS COCO and PASCAL VOC, two popular detection datasets, with compression ratios ranging from 0.25% to 5%. Compared to the prior solely existing dataset distillation method on detection and conventional core set selection methods, OD$^3$ delivers superior accuracy, establishes new state-of-the-art results, surpassing prior best method by more than 14% on COCO mAP$_{50}$ at a compression ratio of 1.0%. Code is available at https://github.com/VILA-Lab/OD3.

Published as a conference paper at ICLR 2026
O OPTIMIZATION-FRE DATASET DISTILLATION FOR OBJECT DETECTION
Salwa K. Al Khatib', Ahmed Elhagry"*, Shitong Shao Zhiqiang Shen' Moohamed bin Zayed University of Artificial Intelligence
"Hong Kong University of Science and Technology (Guangzhou) (salwa shatib, ahmed. elaagry, chiqiang shen) @mbzuai. cetzuaii.ac ac. sshao21 connect. hkust -ww.edu..cennnnnnnemomue edu.cn
ABSTRACT
Training large neural networks on large-scale datasets requires substantial compu- tational resources, particularly for dense prediction tasks such as object detection. Although dataset distillation (DD) has been proposed to alleviate these demands by synthesizing compact datasets from larger ones, most existing work focuses solely on image classification, leaving the more complex detection setting largely unexplored. In this paper, we introduce OD a novel optimization-fre data distil- lation framework specifically designed for object detection. Our approach involves stages: first, candidate selection process in which object instances are itera- tively placed in synthesized images based on their suitable locations, and second, candidate screening process using a pre-trained observer model to remove low- confidence objects. We perform our data synthesis framework on MS COCO and PASCAL VOC, two popular detection datasets, with compression ratios rang- ing from 0.25% to 5%. Compared to the prior solely existing dataset distillation method on detection and conventional core set selection methods, OD3 delivers superior accuracy, establishes new state-of- tate--of-the-rr the-art results, surpassing prior best method by more than 14% on COCO mAP30 at compression ratio of 1.0%. Code is available at htttps: /tt::///iiiii /tgtttttbbbb.... com LA Lab/oD3
INTRODUCTION
Deep neural networks have achieved remarkable performance across a wide range of computer vision tasks (He al., 2016 Ren 2015 Doso- vitskiy 2020: Kirillov et al. 2023), but train- ing these models generally requires substantial computational and data resources. Conventional strategies often involve collecting increasingly large datasets (Deng et al., 2009) and training ever larger networks (Dehghani al. (023) to capture data complexity. This paradigm is par- ticularly evident in object detection (Shao et al.) 2019), where the need for rich annotations, such as bounding boxes even instance masks, can greatly increase dataset sizes and labeling over- head. As result, there a growing interest in techniques that enable the creation of smaller, more manageable datasets capable of approximating the performance achieved by training on the orig- inal data. One promising direction in this area is dataset distillation (DD), which aims to synthesize condensed datasets that are significantly smaller yet still effective for training.
K-Center
Heen Average Precaion (msP)) [X]
Rendom
25
OD3 (Ours)
16.14
1.26
surpassss
0.23%
0.5%
19c
Dateset Compression Ratio (IP))
Figure 1: Performance Comparison of OD on COCO. We compare the mAP performance of our method to others with different compression rates. The upper bound is 39.8% on the full dataset.
The majority of DD approaches have focused on image classification, where each image contains an object or dominant label. This narrow scope overlooks the complexity and diversity of more demanding tasks, specifically object detection. In contrast to classification, detection requires localizing and identifying multiple instances of potentially different classes in a single image. This jump in task complexity involves learning mapping from image to label and predicting boxes and
Equal contribution. 'Corresponding author.

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Published as a conference paper at ICLR 2026
Featuze pyramid network
Original Dataset
2apcapaong Channel-wiie Label E R (N4,)
BBax Clevs Label E & ((())
IN Maumee of SampZea
Number Channela
Seft Label of Condenved Dateset
Candidate SilectionsFiacemen
SEALE
Tocker
wadtlar
Arbk
Candidate Screening
bullding
Obeerver model
Saght
Supervision label
Cutioo
Enowet!
Target
abjacts
Model
Backbone
Condensed Dataset
Detector (xandon init.)
Det
(pre-trained)
Loss
Canvas
owrlap T
Iteration
Condensed Dataset
Post-cvaluation
Figure 2: Illustration of the OD3 framework. In initial stage @, each object in x; E T assigned random location in the synthesized image & E S for 1,.., IPD, and its overlap with existing candidates is checked to decide placement. After IPD synthesized images are initially constructed, pre-trained observer model produces predictions for screening. The observer iteratively evaluates the current canvas to identify and remove objects that do not meet expectations to align with the post-evaluation process. For final reconstruction, the objects are inserted using their bounding boxes into &, E S. Post-evaluation of is carried out by fast distilling knowledge (Shen & Xing 2022) from the observer model to target network using PKD (Cao et al. 2022) loss the respective feature pyramid networks.
class labels for multiple regions within the same image. Consequently, methods that successfully distill datasets for classification often struggle to adapt to the richer problem space of detection.
Another critical distinction lies in the type of supervision and evaluation metrics used in object detection tasks. While classification tasks use labels that can be applied at the image level, detection tasks rely on spatial annotations that align individual objects bounding boxes, complete with class labels. This requirement introduces additional challenges when creating distilled datasets, as both the geometry (location) and identity (class) of objects must be preserved or effectively synthesized. Approaches that merely compress high-level category information may fail to capture the crucial spatial relationships and visual diversity that define detection tasks.
In light of these complexities, we propose Optimization-frie Dataset Distillation for Object Detection (ODS), novel framework explicitly tailored address the unique challenges of synthesizing small, high-fidelit datasets for object detection. The framework leverages instance-level labels with scale- aware dynamic context extension (SA-DCE) to reconstruct diverse training images guided by an observer model, which is grounded in two core ideas: (1) an iterative candidate selection process that strategically places object instances in synthesized images, and (2) a candidate screening process powered by a pre-trained observer model, which discards low-confidence objects. By removing the need for complex optimization procedures in constructing these synthetic images, OD provides a more streamlined and adaptable approach to DD for dense prediction tasks. The main contributions of this work are as follows:
We propose novel DD framework specifically designed for object detection, named 3D It involves a two-stage process: candidate selection, where masked objects are localized and selected based on minimal overlap, and candidate screening, where pre-trained observer filters unreliable candidates.
OD3 bridges a crucial gap by extending the concept of dataset distillation beyond the relatively well-explored territory of image classification to the more challenging domain of object detection in a training-fres scheme. Through a carefully designed process that handles both the spatial and semantic requirements of detection, our framework enables significant reductions in dataset size without sacrificing performance significantly.
We evaluate our framework on MS COCO with compression ratios ranging 0.25% to 5% and PASCAL VOC from 0.5% to 2.0%. The results demonstrate that our framework
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effectively reduces dataset size while maintaining model accuracy, providing efficient solution for training object detectors.
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RELATED WORK
Coreset selection has emerged as one solution for reducing dataset size, primarily in image clas- sification (Guo al.) 2022. Braverman et al. 2019; Huang al., 2019). It shows challenges in object detection, where multiple objects may appear in a single image. Recently, CSOD (Lee et al. 2024) introduces Coreset Selection for Object Detection, which selects image-wise and class-wise representative features for multiple objects of the same class using submodular optimization. Sim- ilarly, Training-Freee Dataset Pruning (Anonymous 2024) addresses dataset pruning for instance segmentation, tackling pixel-level annotations and class imbalances without training. However, these methods often achieve low compression ratios, typically above 20%. In contrast, our proposed distillation method compresses the original dataset to 0.5% or less.
Currently, efforts in dataset distillation for object detection remain limited, unlike in image classifica- tion (Wang et al. 2018: Cazenavette et al. 2022a Sun et al., [024]] Cazenavette et al. 20226). The first framework DCOD (Qi et al. 2024) was proposed for this purpose. DCOD) employs two-stage process: Fetch and Forge. During the Fetch stage, object detection model is trained on the original dataset to extract essential features for localization and recognition tasks, similar to the squeezing process in L (Yin et al. 2024). In the Forge stage, synthetic images are generated via model inversion, embedding required information into the images through uni-level optimization.
METHOD
Preliminaries: Dataset Distillation for Object Detection. The goal of OD3 is to compress a large object detection dataset T= (xx, ca.......))).............. (.. })]] (i=
(.......,,77 into much smaller
synthesized dataset {(x,; (<bj1, ),....)))) (j 1, 1,..., [PD]) that maintains the significant characteristics of T in terms of overall performance, where b = (x.. ce w. h) represents the center of the bounding box and the width and height of the image. Here, |8| < [T] and IPD the notion of images per dataset which reflects the compression ratic"] The performance of model with weights As trained on S should be similar that of a model with weights 0y trained on T, within a small margin EDD. This can be expressed as:
supllCe, - Los (x...)))T EDDD
(1)
with L representing the loss function, and (x., y.)E 'T' is some test or val set associated with T. Definition 1 (Opumization-free dataset distillation for object detection). Our objective is to collect as much effective information as possible on "blank canvas", interpreted as an initially empty image. The information is considered "effective" if it contains sufficient high-quality (high-confidence, well-sized) objects.
Original Dataset
Information Density. To quantify how thor- oughly a canvas is occupied by valuable ob- jects, we define an Information Density function (xx):
T
DRgINOHE
J
ragmeent
TIPD
sugment
((x) 9 (x)( a(x)
(2)
OD sampling
where x is the current canvas (image) under con- sideration. fo(: is well-trained object detector parameterized by 0. g(-) is a function that ag- gregates detection confidence scores across all detected objects. a(x) denotes the combined area of all detected objects on x.
Synthesiaed Dataset 5
Figure 3: Illustration of our sampling controller. ensures that the same object is not placed in different distilled images. The original dataset is divided into IPD segments. Each segment is dis- tilled into a single image, resulting in compact S.
Concretely, we instantiate g(-) and a(.) as fol- lows:
We define I IPD (images per dataset) instead of conventional IPC (images per class) used in classification task as in object detection each image can contain mujti-object with different classes.
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+(x) E (())))((o))) Σx= a(o,)
where K is the total number of objects placed on the canvas x, O, is the r-th object, a(o,) represents the area of object or, ((or) is the detector-derived confidence score for o, Thus, 2(x) measures how confidently and extensively the canvas is occupied by objects.
Information Diversity. In addition to confidence and size, we also encourage diversity of objects on the canvas. We define a simple Information Diversity N(x) by:
N(x) N.
where N is the number of distinct objects on the canvas x. Even when a few objects exhibit high confidence, having more distinct objects can yield richer training signals, making the distilled data more robust.
3.1 OD FRAMEWORK
Overview. Unlike prior dataset distillation methods, our approach begins with a blank canvas as the starting point for generating each new synthetic data sample. As shown in Fig. 2 the data distillation process first proceeds with candidate selection stage (orange box, bottom-left), where object instances are extracted from an existing large-scale dataset T. For each image x, E T, bounding boxes {b:1: bi,..... bik} (K is the number of bounding boxes) capture potentially useful object patches. These patches are fed into localization operation, random yet controlled placement mechanism that carries out M attempts of inserting each candidate onto a reconstructed canvas without exceeding the overlap threshold. Fig. 3 shows the sampling strategy that ensures that [S] = IPD and that objects in S are all unique. This segment-based synthesis strategy also helps preserve the inter-class balance and intra-class diversity for the distilled data. It yields a large pool of candidate patches (b,, () on the canvas, where is the bounding box label or class. Our illustration also highlights how some patches that fail overlap constraints are discarded.
Candidate Screening via Iterative Transfer and Filtering Process. Once a preliminary recon- structed image is assembled, the process proceeds to the candidate screening / filtering stage. Here, an observer model (a pre-trained detector) performs inference on the partially reconstructed canvas. Its predictions are matched with ground-truth boxes that originated from the bounding boxes inserted into the image. Objects that fail to meet confidence or consistency criteria are removed, refining the canvas into a high-quality, diversified arrangement of objects. As result, the final reconstructed image &, E S now contains only those patches that pass the screening process. Also, the bounding box and class annotations associated with these patches are transformed into soft labels, enabling more nuanced supervision in subsequent stages.
Soft Label Generation. Logit-based based soft labels play a critical role in improving the performance of validation models trained on condensed datasets in image classification (Yin et al. 2024) through KD framework (Hinton) 2015). However, when applied to object detection tasks, logit-based soft labels fail to deliver competitive accuracy. This raises the necessity of developing specialized soft label design tailored explicitly for dataset distillation in object detection. The most typical kind of soft label used in object detection is the output of the feature pyramid network (FPN). This output yla can be defined as C M xW where C, H and W represent the number of channels, the height of the canvas and the width of canvas, respectively. Once the obtained, it is employed during the post-evaluation phase and supervised using the following loss function (Shu et al. 2021):
soft label {yfem) has been
Cisc E ( Ily! feat
f phackbone (x.))
(5)
where ((x;, (feat), pfpm and pbackbone refer to the condensed dataset, the FPN in the model and the backbone of the model, respectively. However, we observe that this form of soft label is hard to provide sufficient information for detection.
Thus, we consider a channel-wise soft label for enhancing the performance of the evaluated detector. We leverage the simple pearson knowledge distillation (PKD) (Cao et al., 2022) as a basis for
II our paper, i and are the image index of original dataset, index of distilled image, and object index, respectively.
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designing the soft label generation mechanism object detection. Given this, give the form of soft label as { [ pluabir )) meant ( (("(((xahe((x)))) ), where mean(-), std(.) and c denote the mean
std s (x (x.))+++
operator in the height and width dimensions, the standard deviation operator in the height and width dimensions, and very small amounts, respectively. Finally, the condensed dataset and its associated soft labels are used to train new detector initialized randomly to test how well this small synthetic dataset supports the downstream detection task. As shown in the post-evaluation stage, the condensed dataset supervises the target detector training, and PKD used in post-evaluation can be formulated as:
prpp ( pbackbonne (x,)) - mean ff ( (fhaccboone (x:))
Lmse 18 (x.. (oo)
y:
fent
(6)
std (f (backboone (x: x)) +
1
Scale-aware Dynamic Context Extension. We also propose simple scale-aware dynamic context extension (SA-DCE) for varying sizes of objects in detection- baadd dataset distillation as a crucial enhancement that directly addresses the challenges posed by small objects with limited contextual information. Unlike the optimization-base amplify contextual cues due to their reliance on fixed gradients and pixel-specific updates, context extension involves intentionally expanding the bounding region around objects. It can be formulated as function of the object's s size:
method (Qi et al. 2024), which struggles to preserve
a(o,,) amin
Eextension
F(ii., F)
x T,
Umax
Umin
where F is the SA-DCE function, r E R is scalar representing small pre-determined num- ber of pixels, a(o.,) is the area of r-th object in i-th image, and mmax and miin represent the maximum and minimum areas of objects in T. An example of SA-DCE can be seen Fig.
Original Object
Extended Object
This subtle yet impactful modification adds pe- ripheral context that is often missing, especially in small object representations, providing the model with additional spatial cues to help in ac- curate detection. By extending the context, our model can better differentiate objects from the background, leading to improved performance, particularly in complex scenes. Optimization- based methods inherently lack the flexibility incorporate such targeted context adjustments, as they are confined to the synthetic data representation derived from iterative pixel tuning.
Calnless
Reconstructed Image
Figure 4: Extended bounding box for more ob- jeet context. The figure shows a reconstructed image along with an extended object using the SA- DCE function better capture the object's 's context.
Objective. We combine the two metrics in Eq. and Eq. 4 into single objective for data distillation:
Sº arg max + (XT) + (xT),
where XT denotes the final condensed canvas (i.e., synthesized image) after T synthesis iterations. In practice, we do not explicitly find their optimal values separately, as they are mutually restrictive and entangled. Once the size of the canvas is predefined, we can simply perform an ablation study on the overlap of objects on canvas for the optimal value that maximizes Sg, detailed the following section.
During the iterative data-synthesis process, we update x; for 0, 1,..., ......TT using:
X++1 = fremove (x,)) i 0,
Here, f-da(-) adds new candidate objects to the current canvas, fremove (-) filters out low-confidence or redundant objects, thereby refining the composition of x;-
Iterative Synthesis Methods. We consider two iterative processes for building the final canvas Xy.
1. First Form: Add-Only. The process of this startegy is:
x;+1 fada (x:), = 1,...,
(10)
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In this scenario, newly added objects remain the canvas even if their confidence is low and if they overlap other objects smaller than т. The final objective value is
G1 = +(}+) + (x
(11)
2. Second Form: Add-Then Then-Remove. distilled images:
The Add-Then- Sid-Then-Remove a loop to construct then refine
X fremove (x)), i == 0, T
(12)
Here, each iteration first adds new objects, then filters out objects whose confidence ((o,) is below a threshold 17, or that fail other criteria (e.g., excessive overlap). The final objective value is
G2 क (xx7) + V (x#)
(13)
The following theorem states that incorporating the remove step will positively increases the objective, under enough iterations and a well-chosen confidence threshold.
Theorem 1. (the proof in Appendix Under the above definitions, we have
G2 2 G1.
(14)
Intuition. Because adding a removal step fremove() (-) after every object-addition fada(-) enables more refined composition of the canvas, the second form is guaranteed to achieve at least as high an objective value as the simpler first form (which lacks removal step). That is, the second iterative scheme (add-ther -hen-remove) achieves an objective value at least as large as the add-only approach, under typical assumptions how objects are added removed.
fiemove is an operator that detects objects in the canvas x, and removes those with confidence below a threshold r7. Concretely;
Sketch Proof. Setup:
Step-1: Detection step. Compute fe (x,), i.., run pre-trained detector on the current canvas X2.
Step-2: 2: Scoring each object. For each object os, in (where 1...., K, K is the number of objects), obtain a confidence score (o,.).
Step-3: Threshold partition (no overlaps assumed). Divide the objects into two groups O and O2, one with a confidence level greater than the threshold r,, and the other with a confidence level less than equal to the threshold ry:
O {oipr .i...... Oixe where g (oig) S .... S (0Esr) <n7
O₂ = {uiM+i: 0,, oig.
(15)
where 1/ S q (0is4:)) S 4(000442) S... (0ik)
Step-4: Removing low-confidence objects. All objects whose confidence < n) are discarded. Thus the )((0))
information density on the canvas changes as follows: ΣNO a o1, or.)
ΣK a
Σ. a 04
ΣK
a (o.,)
Comparison of Densities. can interpret
To see why the new density (after removal) is generally higher equal, we as a probability weight, letting Oú denote the area X score contribution of Oig
object kt Removing those objects whose confidence below essentially removes low-quality (score area) contributions from the numerator, thereby increasing the average expected confidence. If K is sufficiently large, we can consider: E, [g (o;,)) ) and E,2 [g (ox,)), a standard probabilistic argument shows that the expected confidence of the surviving set { (Oiar ,ig } is at least as high that of the entire original set. Formally,
E. M4 [1(0).)]
2 [g(0,,)))]
(16)
which implies (xx) 2 + (x--1) 2
1 (xo) in the add-then add-thhe-rremow remove scheme.
By similar reasoning (via probabilistic bound whether the leftover portion remains undetected), one can show that the presence of overlaps does not harm the objective in the
scheme. Hence, G2 > G1 even when overlaps are considered. More details are in Appendix.

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Published as a conference paper at ICLR 2026
Table 1:
Performance Comparison on MS COCO. The compression ratios range from 0.25% to
1.0%. The observer model and the student model are Faster R-CNN-101 and Faster R R-CNN-50.
IPD
Method
mAP (%)
mAP5o (%)
mAP1s (%)
Random
3.50
9.70
1.60
Uniform
3.60
9.80
1.60
K-Center
1.70
6.30
0.40
0.25%
Herding
1.70
5.80
0.50
DCOD (Qi al. 2024
7.20
17.20
4,80
(Ours)
-.. (o.
12.10:F0.3<r.3)
Random
5.50
14,20
2.90
Uniform
5.60
14,30
2.90
K-Center
2.80
8,90
0.5%
Herding
2.60
8.80
0.80
DCOD et 2024
10.00
21.50
8.00
OD? (Ours)
17.20 H.
16.90:90.1:e200
Random
8.30
19.70
5,30
Uniform
8.40
19.70
5.40
K-Center
4,00
12.90
1.20
Herding
4.10
12.50
1.30
DCOD at. 2024
12.10
24.70
10.40
OD? (Ours)
22,40 (+30))
39.50
22.90 (e31
Whole Dataset
39.80
60.10
43.30
Table 2: Performance Comparison on Pascal VOC (mAPso %).. The compression ratios (IPD)
range from 0.5% to 2.0%. The observer and target model are both Faster R-CNN50.
IPD
Random
Hlerding
K-center
Uniform
DCOD (Qi et 2024
OD (Ours)
0.5%
15.80
12.60
15,80
37.90
08.50=0.11.00
1.0%
25.50
19.30
21.90
25.70
46.40
51.10-0.22
38.70.10.1..88
Whole Dataset
80.35
EXPERIMENTS
Experimental Setup. We evaluate OD with compression ratios ranging from 0.25% to 5% for MS COCO (Lin et al. 2014) and from 0.5% to 2% for PASCAL VOC (Everingham al. 2010). We set the overlap threshold to 0.6 in the candidate selection stage, the confidence threshold to 0.2 in the candidate screening stage, and M 40. The foreground objects inserted into the reconstructed images using their ground truth bounding boxes with extended context using SA-DCE. The backgrounds the reconstructed images are randomly sampled from the respective datasets. Synthesis experiments are conducted on single 4090 GPU. The canvas sizes used are 484 x 578 for MS COCO and 375 500 for PASCAL VOC, which are the average width and height of the respective full training sets. For the post-evaluation stage, we use VOC2007 and VOC2012 train/val splits combined for synthesis and VOC2007 test set for evaluation. We use standard COCO metrics (m.PP, mAP s0. and AP7)) along with size metrics (m.P,, mAP. m. and m A AP)) for the COCO dataset. We use Pascal VOC style mAP and mAPso with the area method that uses all points in the precision-recall curve instead of only 11, which provides a precise evaluation (Everingham et al. 2010). Each experiment in Tables [and was run 4 times with different random seeds. We report the mean and standard error of the mean (1 SEM) across these runs. Faster R-CNN-50 models trained for 96 epochs and the RetinaNet-50 models for 256 epochs. All post-evaluation experiments are conducted 2x 4090 GPUs, which highly resource-efficient. Our implementation is based on
the mmdetection (Chen et al. 2019) and mmrazor (Contributors, 2021) frameworks.
Image Generation Time and Efficiency. Our synthesis process is highly efficient compared to optimization- approaches like DCOD (which did not report generation time). Our primary time overhead comes from observer screening. Specifically, generating the condensed dataset takes approximately 4.7 hours on MS COCO and 0.74 hours on PASCAL VOC using single 4090 GPU.
4.1 EXPERIMENTAL RESULTS
Table I presents the comparative results of our method on MS COCO (Lin et al. 2014) with core-set selection methods and with DCOD (Qi et al. 2024). The core-set selection methods include: random initialization (Rebuffi al. (017). Uniform (Lee al.) 2024), K-center (Sener & Savarese (017). and Herding (Castro et al. 2018 Chen et al. 2012). Our method, OD3, outperforms all other methods across various compression ratios (TPD) ranging from 0.25% to 1.0%. Notably, at 1.0%, we achieve
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Table 3: Ablation Study on Label Type. The impact of using mask labels, bounding box (BBox)
labels, or Ex-BBox in the data synthesis process across various compression ratios. Ex-Bbox represents the BBox with the extended context using SA-DCE.
IPD
Label
mAP
mAP50
mAP
mAP,
mAP.
mAP:
Mask
10.90
20.80
10.30
3.50
15.10
15.90
0.25%
Bbox
12.40
23.90
11.60
4,90
16.10
18.10
Ex- Ex-Bbox
42.90
24.30
5.60
16.80
17.70,-0.)
Mask
15.10
28.00
14.80
5.60
20.30
22.30
0.5%
Bbox
16.60
30.30
16.30
6.80
21.70
23.30
Ex- Bbox
47.20
31.60
[6.90
8,40
23.00
22.80,
Mask
21.20
37.40
21.30
8.90
26.40
29.90
1.0%
Bbox
22.00
38.70
22.30
9.70
27.40
30.10
Ex- Bbox
22.40
39.50).a.8)
22.00
10.60
28.00
29.80;
Mask 30.00
49.50
31.70
15.10
34.80
39.10
5.0%
Bbox 29.90
49.40
31.50
15.00
34.70
38.50
Ex-Bbox
,0.24
49.30
e6.24
34.90
38.40,-u.1)
Table 4: Cross-Architecture Performance Comparison for OD3 use ResNet101 and target models use ResNet50.
MS COCO. Observer models
IPD
Observer
Target
mAP (%)
mAP3o (%)
mAP15 (%
RetinaNet
RetinaNet
13.90
25.30
13.50
Faster R-CNN
RetinaNet
14.50
25.70
14.30
0.25% %
Deformable DETR ViTDet
Faster R-CNN
11.90
22.60
11.10
Faster R-CNN
11.00
21.30
10.10
RetinaNet
RetinaNet
18.40
32.50
18.20
Faster R-CNN
RetinaNet
17.40
30.20
17.60
0.5%
Deformable DETR
Faster R-CNN
16.20
29.50
16.00
ViTDet
Faster R-CNN
15.90
29.20
15.60
RetinaNet
RetinaNet
22.20
37.90
22.60
Faster R-CNN
RetinaNet
22.20
37.40
23.00
1.0% %
Deformable DETR
Faster R-CNN
22.00
38.00
22.90
ViTDet
Faster R-CNN
21.70
38.30
21.80
substantial 14.8% improvement in mAPso over DCOD. Furthermore, our method consistently outperforms other core-set selection methods, with mAP3o improvements of to 27.0% at 1.0% IPD. Our method also achieves the highest performance compression ratio. Since the authors of DCOD did not report its performance the size metrics of MS COCO, we are unable to compare the methods in that regard. Nonetheless, these results underline the effectiveness of OD in achieving superior performance across range of compression ratios. We also report results on PASCAL VOC in Table 2 with different IPDs, where the method achieves 70% mAP at 2.0% compression, surpassing DCOD by 8.0%. Qualitative visualizations of the synthesis from our 3D3 on MS COCO are shown in Fig.
mAP., m.A and m.P at each
4.2 ABLATION STUDIES
Label type. The type of label used when inserting the candidate objects into the synthesized image is studied in Table 3 We consider three types of labels: mask-level label, BBox-level label, and Ex-Bbox which reters to BBox with extended context using SA-DCE (refer to Sec. 3.I.. Using
Figure 5: Qualitative results of the synthesis process of OD3 MS COCO. Initial backgrounds of the canvas are randomly selected from the training set, and objects are inserted using their bounding- box level labels. Those images generated at 0.5% IPD.
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Table Ablation Study on Method Components. We highlight the impact of candidate selection and screening on MS COCO performance across varying compression rates.
Candidate Selection
Candidate Sereening
mAP30
mAP7s
mAP,
mAP in
mAP;
X
X
0.90
2.40
0.40
0.00
1.10
1.20
0.25%
9.70
19.10
8.90
3.70
13.10
13.60
v
12.90
24.30
12.10
5.60
16.80
17.70
x
2.00
4,00
1,90
0.10
2.60
3.10
0.5%
X
14.50
27.30
13.80
5.90
19.30
20.30
/
V
17.20
31,90
16.90
8.40
23,00
22.80
X
X
7.50
14.10
7,30
0.9
8.90
13.30
1.0%
X
19.00
33,90
19.10
8.70
24.40
25.70
/
22.40
39.50
22.90
10.60
28.00
29.80
X
x
8.60
17.80
7.30
1.10
10.40
16.40
28.10
46.70
29.60
14.00
34.00
37.00
30.10
49.70
31.80
16.20
34.90
38.40
Table 6: Performance across higher compression ratios MS COCO. While main experi- ments evaluate extreme compression regimes (0.25%-1.0%% IPD), this table reports performance at larger compression ratios (5.0%-20.0%) demonstrating that OD continues scale predictably as more synthesized data are allocated.
IPD
mAP (%)
mAPso (%)
mAPrs (%)
mAP, (%)
mAP.. (%)
mAP; (%)
5.0%
30.10
49.70
31.80
16.20
34.90
38.40
%
31.80
$1.70
34.10
16.80
36.70
40.50
20.0%
32.60
53.00
34.80
18.00
37.50
Whole Dataset
39.80
60.60
43.50
22.40
43.90
51.90
BBox labels outperforms the mask labels across all IPDs except for 5.0%, where their performance converges to similar level. This is because Bbox labels preserves local context and surrounding environment of individual objects, providing models with additional cues for recognizing objects. Using Ex-Bbox further improves performance across all IPDs, where improvement of 1.8% in mAPPs can be seen at 0.5% compression. When specifically analyzing the size metrics, the extended context benefits small objects the most on the account of large objects, which bridges the substantial gap between their detection performance.
Overlap threshold. Fig. 6 shows how different values of overlap thresholds 7 in candidate selection affect the performance of our method across various compression ratios. It can be seen that 0.6 consistently optimizes m.APsa and mAP. This value can be thought as an optimal trade-off between $ and (x).
Cross-architecture generalization. To assess the generalization capabil- ity of our condensed datasets, we con- duct experiments with Faster R-CNN (Ren) 2015), a two-stage detector and RetinaNet (Lin) 2017). one-stage detector. Table shows that the dis- tilled datasets generalize in hetero- geneous settings, where the observer model is two-stage detector, and the target model is a one-stage de- tector, across varying compression ra- tios from 0.25% to 5,0%. The results demonstrate that performance on Reti- naNet is comparable to that on Faster for the Faster R-CNN observer and RetinaNet target configuration reaches 25.70% surpassing the 24.30% obtained in the Faster R-CNN observer and target setup. At higher compression ratios, such as 1.0% and 5.0%, RetinaNet continues to yield competitive results, achieving mAPss scores of 37.40% and 48.60%, respectively. In addition, evaluate ViTDet (Li et al. (022) and Deformable DETR (huu et al. 2020), transformer baased detectors, as observer models. For instance, ViTDet achieves strong performance with Faster R-CNN target model, reaching mAP scores of
12.75
22.5
30.0
12.50
12.25
12.00
21.5
29.4
40
24.4
0.25%
15
-0-
5%
224.0
49.0
E23.6
30
U.3
0.4 0.0
03
Figure 6: Ablation study of overlap threshold 7. mAP and mAP are evaluated at different thresholds used in candidate selection with compression ratios ranging from 0.25% 5%.
R-CNN across all IPDs. Specifically, at 0.25% IPD, m.AP3o

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Published as a conference paper at ICLR 2026
21.30%, 29.20%, and 38.30% at 0.25%, 0.5%, and 1.0% IPD, respectively, despite their architectural differences. The results demonstrate that our method maintains transferability across fundamentally different model paradigms, further highlighting the robustness of the distilled datasets and their effective applicability across diverse architectures.
Method Components. Table presents ablation study evaluating the impact of candidate selection and candidate screening on the MS COCO performance across varying compression ratios (IPD). The table entries where both are not used correspond when all objects from the training set are randomly assigned a location and inserted into the distilled images without any filtration. The results demonstrate the effectiveness of both components improving the quality of the synthesized dataset. The addition of candidate screening further improves the results across all compression ratios. For example, there is 3.4% and a 5.6% increase m.P P and m. P30 for the 1.0% distilled dataset.
More IPD budgets. Table 6 summarizes performance when training on distilled datasets and the IPD budget is increased beyond the extreme compression regimes used in our main experiments (0.25% to 1.0%). As more synthesized data are allocated (5%-20% IPD), performance rises steadily across all COCO metrics, indicating that OD scales in a predictable and stable manner. Although these larger budgets approach the performance of the full dataset, consistent gap remains, which reflects the intrinsic challenge of reproducing the full diversity of COCO using synthesized samples.
CONCLUSION
In this work, we introduced new OD3 framework for optimization -feee dataset distillation in object detection, achieving significant improvements over existing methods. Using a novel two-stage process of candidate selection and candidate screening driven by a pre-trained observer model, our framework strategically synthesizes compact yet highly effective datasets tailored for object detection. Our method consistently demonstrated superior performance across multiple evaluation metrics, For instance, OD achieved more than 14.0% improvement in mAPs0 compared to the state-of-the-art method DCOD on MS COCO.
ETHICS STATEMENT
This work uses only publicly available, appropriately licensed detection datasets (e.g., COCO, Objects365) and does not involve new data collection or identity inference. Distilled data can inherit and potentially amplify biases present in the sources, we therefore report detailed metrics and provide extensive distilled visualizations to monitor this. The method is not intended for surveillance or biometric identification. To reduce environmental impact, our optimization-fre -fee distillation aims to lower compute and storage costs.
REPRODUCIBILITY STATEMENT
For reproducibility, we will release: (i) code with exact scripts and command lines for generating distilled sets and training/evaluating detectors, (ii) pinned dependencies and conda environment file, (iii) random seeds and deterministic flags, (iv) the distilled datasets themselves, and (v) details reproducing all tables/figures. We detail data splits, preprocessing, evaluation and report mean, +, std over multiple runs. Ablation scripts cover distilled size and cross-architecture transfer (e.g., Faster R-CNN, RetinaNet, etc.).
ACKNOWLEDGEMENTS
This research was supported by the MBZUAI-WIS Joint Program for Al Research.
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APPENDIX
A LIMITATIONS AND SOCIETAL IMPACTS
There are many potential societal impacts our work, such improving the accessibility of effi- cient datasets for academia with limited computational resources, and fostering the development of sustainable AI. While our method does not introduce direct negative consequences, it important to acknowledge that object detection technology can be misused- particularly in surveillance applica- tions that infringe on individual privacy. The increased efficiency and scalability enabled by dataset distillation may unintentionally lower barriers for deploying such systems at scale. One limitation of our approach the reliance on ground bounding boxes during synthesis, which assumes access to labeled data. This restricts the method's ' applicability in fully unsupervised or label-scarce scenarios.
B ALGORITHM
Our detailed procedure shown in Algorithm First, each object candidate is added to the partially formed "blank canvas" via random. copy-paste. Multiple objects may be overlaid, that visual variety preserved. Next, the observer model runs on this synthesized image and assesses the confidence of each placed object. Low-confidence objects that objects are removed, refining the canvas into more coherent scene. This cycle of add-and-remoo iterates multiple times, driving the canvas toward a final state containing only high-confidence. mid-overlapping objects. Fig. green boxes in screening stage indicate an inserted object is deemed infeasible, applying removal process maintain quality and coherence.
matched the ground truth
Algorithm 1: Optimization Dataset Distillation for Object Detection (OD3)
Input: Original dataset T; Synthetic dataset S; Observer model fobs: Overlap threshold r; Screening threshold 7y: Images per dataset IPD; Canvas (initially Ø and updated constantly with &); Extension e in Eq. 7 Random placement candidate positions (m, nz) for 1,....M! M.
for &, where [S] IPD do
while is not full do
Candidate Selection Placement. for y;) (o0y;)77 do
for < <bi,, Cr, do
bir bir b fir + F(F);
while loU(b' (mi, Ci),C) < and attempts < M do L Place <b'rr Cir C; Exit;
D Candidate Screening Filter objects from C for &;; Sobs (C); for < by, Ck >E C do
if Conf(Soss. by) < 17 then Remove < by, C₁ > from C:
8, C: Append & to S:
Output: Synthesized dataset S
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C DISTILLED DATA DISTRIBUTION STATISTICS
Table presents presents the distribution of images and objects across 12 supercategories in MS COCO under different IPD settings well as in the original dataset. The full dataset (100%) con- tains 64,115 images and 262,465 person-related objects, while the most compressed version (0.25%) retains only 295 images and 5,313 ob-
Peremtage Imaps Robdbe Tutt Daraat PMattPo
food
person
animal
furniture
accnssory
electrocic
sports
appliancs
Indoor
0.04
jects of this supercategory. Similar reductions are observed across all supercategories, demon- strating the significant compression effect of
2,02
the OD3 distillation process. We also present the ratio of the number of images in particu- lar supercategory at a certain compression ratio compared the number of images of the cor- responding supercategory in the original MS
0.5%
Figure 7: Percentage of images in the distilled datasets relative the full dataset.
COCO dataset in Fig. 7
Fig. 8 further illustrates the relative probability distribution of supercategories across dataset versions. Despite significant compression that can be seen in Table [ the distribution remains statistically consistent with the original dataset. This shows that OD does not introduce any inherent bias toward any specific category.
Table 7: Supercategory distribution across different IPD settings. The number of images and objects per supercategory is presented for the MS COCO (Lin et al. 2014) dataset and the OD3 distilled versions. Note that the data in the table the same Fig. but Fig. displays the values as percentages. It can be seen that both the number of images and objects per supercategory are drastically compressed. Supercategory-level data is provided instead of fine-grained categories to maintain clarity and simplify comparisons.
IPD
Type
person
lndoor
Food
Kitchen
appliance
Supercalegury in MS COCO vehicle
Tarniture
animaa
electronic
aecessory
Guildor
sports
100% Datasat)
Images Objects
64115
16255
20792
27358
23980
17691
23218
62.566
1.0%
Objects
Imuges
20158
1183
2876
733
5956
660
5922
948
831
5489
1012
6035
694
4138
396
2047
615
3083
1407
1308
423
0.5%
Objects
Images
10257
585
1570
366
2963
513
2096
463
335
120
3486
496
3032
351
2142
279
848
270
1374
433
752
219
212
595
0.25%
Images
Objects
205
155
142
D MORE ABLATIONS
Table 8 further illustrates the impact of SA-DCE on object detection performance. Our SA-DCE method consistently outperforms both our no-extension and static extension baseline methods. Notably, it improves mAP scores while striking a balance between small and medium object detection. The no-extension setting suffers from reduced performance small objects due limited contextual information, whereas static extension provides slight improvements but lacks adaptability to object scale. In contrast, SA-DCE dynamically adjusts the context extension based on object size, leading to significant gains, particularly in small-object detection. These results demonstrate that SA-DCE effectively enhances detection robustness while preserving overall performance across different object scales.
Table 9 Thighlights the effect varying the confidence threshold (1)) on detection performance. Setting 0.2 consistently yields the best overall results across different IPD values, improving mAP and balancing small, medium, and large object detection. Lower thresholds (n- = 0.1) allow more candidates but introduce noise, while higher thresholds (17 2 0.3) remove potentially useful detections, leading to a drop in performance. These findings demonstrate that careful tuning of 7 is crucial for optimizing detection accuracy.
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0.35
100%
Iooé
uubdour
30.55
11.2
3/2
7.3
5.3
5.9
10.1
7.4
33
1.6
54
acenesory
Parcentage (%)
fumiituue
sports
0.25
Michen
dlectronic
applance
Indoce
Dataset
1.0%
0.15
parson
0.5%
34.9
10.3
2.6
7.3
4.7
10.2
10.1
5.3
0.05
0.23%
36.0
10.3
2.4
6.7
5.2
1.7
11.1
9.4
8.3
3.2
0.8
4.8
Dataset
Peruin
valick
satdber
Aninal
bmm
fond
elecranii
applance
Indtor
Supercategories
Figure Probability distribution of supercategories across datasets. The figure highlights the relative distribution of each supercategory in the original MS COCO dataset (100%) and its synthesized counterparts at different compression ratios (0.25%, 0.5%, and 1.0%). This analysis shows that the synthesis process successfully mirrors the distribution of supercategories, (xx)., in the original dataset.
Table Ablation Study of SA-DCE. The table evaluates the influence extending statically and dynamically (using SA-DCE) the bounding boxes (in pixels) of the objects in the distilled dataset across varying compression ratios the MS COCO performance. Static extension refers to applying constant extension F to all inserted objects regardless of their size. We set as 10 pixels.
Extension (pixels)
IPD
mAP
mAP:;
mAP 75
mAP,
mAP,
mAP;
No extension
16.60
30.30
16.30
6.80
21.70
23.30
0.5%
Static extension
16.80
30.70
16.60
7.40
22.10
22.90
SA-DCE
17.20
31.90
16.90
8.40
23.00
22.80
No extension
22.00
38.70
22.30
9.70
27.40
30.10
1.0%
Static extension
22.30
38.80
22.90
9.90
27.40
30.40
SA-DCE 22.40
39.50
22.90
10.60
28.00
29.80
Objects with confidence lower than Table 9: Ablation Study of Confidence Threshold (1)).
(determined by observer model) are removed the candidate screening stage.
Confidence Threshold (7)
IPD
mAP
MA 50
mAP 75
mAP.
mA
mAP:
0.1
11.10
21.70
10.50
5.30
15.50
14.50
0.2
12.90
24.30
12.10
5.60
16.80
17.70
0.25 %
0.3
10.50
21.00
9.40
4,90
14.80
13.40
0.4
10.00
19.80
9.00
5.40
13.80
12.90
0.5
10.20
20.30
9.30
5.00
14.50
12.90
17.00
31.50
16.60
8.10
22.80
17.20
31.90
16.90
8.40
32.00
22.80
0.5%
0.3
16,20
30.10
15.90
7.30
21.60
21.60
0.4
16.40
30.60
16.10
8.40
22.50
21.90
0.5
15.70
29.50
15.20
7.20
21.30
20,50
0.1
21.70
38.30
22.20
10.80
27.80
28.90
0.2
22.40
39.50
22.90
10.60
28,00
29.80
1.0%
22.00
39.00
22.30
10.60
27.60
29.30
22.00
38.80
22.40
10.20
27.80
28.90
0.5
21.80
38.50
22.20
10.30
27.70
29.00
Table 10 analyzes the effect of canvas size detection performance across different IPD values. The canvas size was selected based on the average width and height of all training images in the MS COCO dataset, with additional smaller and larger canvas sizes included for comparison and
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Figure 9: Qualitative results of the candidate screening process of OD on MS COCO. The screened and removed objects highlighted with light green mask. It be seen that these low-confidence objects are unreliable and can be regarded as "visual noise".
evaluation. Results indicate that while optimal canvas size (484 578) achieves the highest mAP scores, further reduction in canvas dimensions leads to a drop in performance. This suggests that excessively small canvases limit the available contextual information, negatively impacting detection accuracy. Conversely, overly large canvases introduce unnecessary background noise, reducing effectiveness. These findings highlight the importance of selecting balanced canvas size maximize object representation while maintaining relevant contextual cues for dataset distillation.
Table 10: Ablation Study of Canvas Size. The table evaluates the influence of canvas size on the MS COCO performance of the distilled dataset across varying compression ratios.
Canvas Size (pixels)
IPD
mAP
mA P 50
MAP
mAP,
mAP,
mAP;
363 433
10.30
20.80
9.10
4.80
13.60
14.20
484 578
12.90
24.30
12.10
5.60
16.80
17.70
0.25 %
726 x
10.50
20.50
9.50
4.70
16.30
12.50
968 x 1156
8.80
17.50
7.80
3.60
14.90
10,20
363 433
15.40
29.10
14.70
7.20
19.90
21.50
484 578
17.20
31.90
16.90
8.40
23.00
22.80
0.5
726 x 867
15.70
29.10
15.20
7.30
22.60
19.90
968 x 1156
13.70
26.00
12.90
7.00
20.00
16.60
363 433
20,90
37.20
21.10
9.80
25.70
28.30
484 578
22.40
39.50
22.90
10.60
29.80
1.0%
726 x 867
21.00
37.30
21.40
10.80
27.60
26.40
968 x 1156
16.80
30.40
16.90
8.40
24.10
20.40
Figure 9 illustrates samples of objects removed by the candidate- module during the distillation process. Objects with low confidence are identified and masked (shown in light green), revealing how these regions often correspond to ambiguous shapes, partial objects, or background clutter. Removing these "visual noise" segments helps the distilled dataset focus high-quality, semantically stable content, which in turn strengthens the downstream learning signals.
We extend the cross-architecture evaluation from Table Table [[] Results show that DETR-based models degrade sharply under extreme data scarcity, regardless of the nature the observer model used, whereas Faster R-CNN maintains substantially stronger performance. DETR and its variants are known to require large datasets, long optimization schedules, and abundant object diversity to converge well (Wang et al. 2022). When the distilled dataset is extremely small, these models
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Table 11: More Cross-Acchitecture Results with a DETR-based target model (0.5 % IPD). These experiments highlight a trend where transformer-base detectors are substantially more data-hungh than their CNN counterparts.
Observer
Target
mAP (%)
mAP (%)
mAP (%)
Faster R-CN R-CNN Deformable DETR
Deformable DETR
6.00
12.20
5.50
Deformable DETR
9.40
18.80
8.60
Faster R-CNN
Faster R-CNN
17.20
39.50
22,90
struggle to learn effective queries and attention patterns. This may represent valuable direction for future exploration in the DD field.
E PROOF OF THEOREM
Proof. Let f E N be the current iteration index with 0 < / < T. We assume for this iteration that objects placed on the canvas x, do not overlap. the canvas x, contain K objects {or]r=o We sort these objects according their confidence scores g(o,) and partition them into two sets based on a threshold ry:
f0r 01 ......,, Disec where g(o))) I q(o),) S... g(0rr)) ?
(17)
( 01 0ix} where S q((im))) ..... g(oix))
The first set satisfy E, (4 < n)) = M+1 Applying the removal operator fremove discards every object whose confidence is below 77, i..., (oio Oiy. 0im)
Hence, the updated canvas X preserves only those objects whose scores exceed r), and it may then be "refilled" by sadd(.) with new (randomly synthesized) objects from the same distribution as in previous iterations.
Then, we can compare the (xx) of and x++1
x++1- (xx can be described as
Dj= (or, (or,) E +j=M+1 (o,,) q (oi,)
M
(x)
(18)
Σ/z (or)) + Ej=m+4 (ot,)
where M+= (o;,) and Ex M+ s (0i,) a (0i,)) are same for x; and Xe+1- In general, we will fill the canvas at each iteration, so E/= (ois) can also be considered constant. And the
difference between X and Xi+1 is E s(a Ja(ay) where S is the areas of the canvas. Due to 5
EoSisM [p (os,) < m)] = for X4, we can get (EosigM [q (o:,)] E(y] 2 0) = 0, and
M++1 for X(+1- Then, we get BoSjsM (q (oi,) < <n) K
K --1
(BosjsM (g (ox)) - Einl 0)
(19)
Since object is uniformly distributed, so and E are able to swap places. p (Fosism [q (oi,)) 2 m) M for X we can prove that
Because
(xT) + (xT-1) + (xT-2)
4b (Xo)
(20)
Consistency of (x) and overlaps. As T oo, the canvas becomes fully populated in both the add-only and add- teetttheeeeeee strategies, so the number of objects A (xT) is generally similar (i.c., both can fill the canvas to full capacity).
Overlap handling.
When T is sufficiently large, the canvas will necessarily be filled, so it can be assumed that the first form and the second form of / (xy) are consistent. So G2 remains greater than G1. When there are some overlaps of objects in the iteration, the conclusion still holds. For example, object o, and og overlap, and their overlap region is og- The score of o₂ is between q (o,) and q (o,). both g (oa) and y (0b) are larger smaller than y, then both of them will not be considered. If one is larger and
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smaller than 77 (assuming that (oa) and q (ob) 17), then oa is removed and or will also be removed the process of screening. and the portion left behind (i.e.. possibly the mutilated o - og may not be detectable by the detector, it may be successfully detected. Even assuming that this is undetectable for Og (i.e., the confidence score is low), then in the next iteration will still be removed.
Assume that the probability of having overlap with another object is p1- The probability that (6x) Σ T is detected will be P2- This probability of it being removed or not having an overlap in the next iteration is pi + (1 - Pi) p2, which is consistently greater than (1 pi) (1 p2) when P2 2 0.5. If p2 < 0.5, this means that Oş is a qualified sample (detectable by detector or observer) and therefore does not need to be removed.
Thus, in the presence of overlap, G2 remains greater than G1.
F USE OF LARGE LANGUAGE MODELS
An LLM was used only to refine the writing of the paper. All ideas, methods, experimental designs, and results were conducted by the authors.
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Reviews:
[{"review_id": "eyZx6TRMLD", "reviewer": "Reviewer_B2qG", "summary": "This paper introduces $\\text{OD}^3$ (Optimization-free Dataset Distillation for Detection), a novel framework explicitly designed to address the unique challenge of synthesizing small, high-fidelity datasets for object detection, a task more complex than image classification due to its spatial and semantic demands. $\\text{OD}^3$ leverages a training-free, two-stage process—candidate selection followed by screening using a pre-trained \"observer\" model—to strategically reconstruct diverse, labeled training images without complex optimization. Evaluations on MS COCO and PASCAL VOC demonstrate that $\\text{OD}^3$ effectively achieves significant dataset size reduction (up to $99.75\\%$) while successfully maintaining the accuracy of object detection models.", "questions": "1. Tables 1 and 2 should include upper bounds to illustrate the potential for improvement in existing methods within the field of DD for object detection.\n2. Table 4 indicates that the best results are obtained when the observer and target detectors share the same architecture. Employing higher-performance detectors does not enhance the distillation effect. Therefore, it would be insightful to compare the performance of student models that also adopt DETR or other transformer-based architectures—that is, models of similar design.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This framework employs a clever two-stage design that combines iterative candidate selection with pre-trained observer model filtering. This approach avoids the complex and time-consuming optimization processes typical of traditional DD methods, making the dataset distillation process more efficient, straightforward, and easier to deploy.\n\n2. $\\text{OD}^3$ achieves extremely high compression ratios (up to 99.75%) on MS COCO and PASCAL VOC while maintaining the accuracy of trained models, providing an efficient solution for saving computational and data resources when training object detectors.", "weaknesses": "1. The second contribution in the introduction extends the concept of dataset distillation from the relatively well-explored domain of image classification to the more challenging task of object detection. This has already been achieved by DCOD.\n2. Both DCOD and OD3 experience substantial performance degradation compared to their uncompressed counterparts. At compression rates of 5% or even 10%, it remains unclear how much additional compression would be necessary to approach the performance of the uncompressed models.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rMrZahFUC2", "reviewer": "Reviewer_AKz7", "summary": "This paper proposes OD³, an optimization-free framework for dataset distillation in object detection. The method synthesizes a compact dataset by strategically selecting and placing object instances from the original dataset onto blank canvases and then screening them using a pre-trained observer model.", "questions": "see Weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "The proposed \"optimization-free\" paradigm, which employs a rule-based placement and screening strategy, enables extremely fast dataset synthesis and significantly reduces the computational overhead of the distillation process itself.", "weaknesses": "1. The screening module directly removes objects deemed low-confidence by the observer model. However, these low-confidence objects could precisely be the \"hard examples\"—such as rare categories, heavily occluded, or unusually shaped objects—that are crucial for training a robust detector.\n﻿\n2. The final optimization objective does not account for inter-class balance and intra-class diversity, both of which are critical for the object detection task. The current metrics for Information Density (Φ) and Diversity (N) are too coarse and may lead to a distilled dataset that is biased towards dominant or easy-to-detect classes.\n\n﻿3.  The core of the method involves placing objects randomly on a canvas with only an overlap ratio as a constraint. This ignores the spatial layout relationships, relative scales, and co-occurrence contexts of objects in the real world. Consequently, the synthesized images may lack semantic realism, which could limit the detector's ability to learn high-level contextual reasoning.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "FR8Tg6ErUP", "reviewer": "Reviewer_NPmv", "summary": "OD3 is an optimization-free dataset distillation framework for object detection that synthesizes compact datasets by iteratively placing and screening object instances using a pre-trained observer model. It achieves state-of-the-art performance, surpassing prior methods by over 14% in mAP50 on COCO at 1% compression ratio.", "questions": "In Weaknesses", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.OD3 achieves strong performance with clear speed advantages—dataset synthesis on COCO takes ~4.7 hours on a single GPU, significantly faster than optimization-based alternatives.\n\n2.The SA-DCE component provides a practical way to retain contextual cues for small objects, offering a useful insight for dataset distillation in detection.", "weaknesses": "1. The synthesis relies on instance copy-pasting, leading to a domain gap between synthetic and real images that may limit generalization.\n\n2. The method depends on a pre-trained observer model; if this model is biased (e.g., performs poorly on certain categories), it may filter out valid instances and introduce distillation bias.\n\n3. Performance at higher compression ratios (e.g., 5%，10%，20%，100%) is not explored, leaving the scalability and upper performance bound unclear.\n\n4. Backgrounds are randomly sampled from the original dataset, which can create semantically implausible scenes (e.g., indoor objects placed on outdoor backgrounds).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)