Fetch and Forge: Efficient Dataset Condensation for Object Detection
Authors: Ding Qi
Jian Li
Jinlong Peng
Bo Zhao
Shuguang Dou
Jialin Li
Jiangning Zhang
Yabiao Wang
Chengjie Wang
Cairong Zhao
Venue: NeurIPS 2024 poster (2024)
Keywords: Dataset Condensation
Object Detection
Primary area: optimization_for_deep_networks

Abstract: Dataset condensation (DC) is an emerging technique capable of creating compact synthetic datasets from large originals while maintaining considerable performance. It is crucial for accelerating network training and reducing data storage requirements. 
However, current research on DC mainly focuses on image classification, with less exploration of object detection.
This is primarily due to two challenges: (i) the multitasking nature of object detection complicates the condensation process, and (ii) Object detection datasets are characterized by large-scale and high-resolution data, which are difficult for existing DC methods to handle.
As a remedy, we propose DCOD, the first dataset condensation framework for object detection. It operates in two stages: Fetch and Forge, initially storing key localization and classification information into model parameters, and then reconstructing synthetic images via model inversion. 
For the complex of multiple objects in an image, we propose Foreground Background Decoupling to centrally update the foreground of multiple instances and Incremental PatchExpand to further enhance the diversity of foregrounds.
Extensive experiments on various detection datasets demonstrate the superiority of DCOD. Even at an extremely low compression rate of 1\%, we achieve 46.4\% and 24.7\% $\text{AP}_{50}$ on the VOC and COCO, respectively, significantly reducing detector training duration.

Fetch and Forge: Efficient Dataset Condensation for Object Detection
Ding Qi Jian Lift
Jinlong Peng? Bo Zhao
Shuguang Dout
Jialin Li
Jiangning Zhang
Yabiao Wang
Chengjie Wang²
Cairong Zhao 'f
'Tongii University Tennent YouTu Lab
ZZejiang University *Shanghai Jiao Tong University
Abstract
Dataset condensation (DC) is an emerging technique capable of creating compact synthetic datasets from large originals while maintaining considerable performance. It is crucial for accelerating network training and reducing data storage require- ments. However, current research on DC mainly focuses on image classification, with less exploration of object detection. This is primarily due to two challenges: the multitasking nature of object detection complicates the condensation pro- cess, and (ii) Object detection datasets are characterized by scrge-scale and high- resolution data, which are difficult for existing DC methods handle. As remedy, we propose DCOD, the first dataset condensation framework for object detection. It operates in two stages: Fetch and Forge, initially storing key localization and classification information into model parameters, and then reconstructing synthetic images via model inversion. For the complex multiple objects an image, we propose Foreground Background Decoupling centrally update the foreground of multiple instances and Incremental PatchExpand to further enhance the diversity of foregrounds. Extensive experiments the superiority of DCOD. Even at an extremely low compression rate of 1,, we achieve 46.4% and 24.7% AP50 on the VOC and COCO, respectively, significantly reducing detector training duration.
various detection datasets demonstrate
Introduction
In the past decade, the field of deep learning has witnessed the emergence of nigh-performance models, driven by the development of convolutional and Transformer networks [10 24 5 These models,
while benefiting from large-scale datasets, also encounter significant challenges such as data storage and the demand for extensive training resources. Dataset condensation (or distillation) [27 32 has emerged in response, aiming to synthesize a small amount of data that approximates the training effectiveness of original datasets, thus offering hope to alleviate this dilemma.
Current dataset condensation (DC) research mainly focuses on image classification and is divided
into two main frameworks: Meta-learning and Data-matchin [T]] As shown in Figure [[) Meta-learning framework (27
(a), the
34 [5] optimizes synthetic datasets by minimizing performance
risks on the original dataset's validation set, using a bi-level optimization process. contrast, Figure (b) illustrates the Data-matching framework, which aligns gradients (32) feature distributions [31, or training trajectories 2 to simulate the original data's 's impact during different model training stages.
WWrk done when Ding Qi is an intern in Tencent YouTu Lab, and the advisor is Jian Li. Corresponding author
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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Classification
Object Detection
T
Meta Train
Meta Test
Fetch
felss
(a) Meta-leaming Framework
Stage
Stage
Inversion
g
Amatch
Grad Dist Trajectory
Forge
T
det Rreg
(b) Data-matching Framework
(c) Our DCOD Framework
Forward Pass
Synthetic Set 00) Trained on $ 0 g(0) Initialized Parameters
Backward Pass J Original Sct 0, Trained on T
Freeze Parameters
Figure 1: (a) Meta-learning treats synthetic set updates in DC as a meta-task; (b) Data-matching indirectly matches gradients, feature distributions, and training trajectories generated during network training; (c) Our proposed DCOD, the first for object detection, decouples the bi-level optimization of methods and b, following a Fetch and Forge two-stage approach.
While both frameworks above make sigmificant contributions to the field, they share common limita- tions. The complexity and computational expense of their bi-level optimization process restrict their application, typically confining them to smaller datasets (c.g., CIFARIO, CIFAR 100, Tiny-ImageNer and simpler network architectures (e.g., ConvNNe-33 AlexNet, VGG-11, ResNet- 18). This limitation is particularly pronounced in object detection tasks, which are inherently more complex than image classification. Specifically. i) the requirement in object detection for simultaneous localization and classification of multiple objects in an image considerably complicates the condensation process; and ii) the lcrge-scale, high-resolution nature of detection dataset images, along with the complexity of detection network structures, poses significant challenges in extending existing DC methods.
In this study, we propose DCOD, the first dataset condensation framework for object detection. DCOD is distinct in its ability to flexibly generate foreground objects of varying sizes, shapes, and categories at any position within an image, as demonstrated in Figure Additionally, DCOD streamlines the process by eliminating the complexities of traditional bi-level optimization, enhancing its compatibility with complex detection networks and large-scale, high-resolution data. DCOD operates in two stages: Fetch and Forge. As in Figure (c), in the first stage Fetch, we train an object detector on the original dataset following a standard procedure, during which key information from the original data is stored within the detector. Then, in the second stage, we employ the model inversion to synthesize images from the trained detector. Specifically, we freeze the detector's weights and input initialization images and labels (including position, size, and category) randomly sampled from the original dataset to capture the instance distribution real originals. During the inversion process, we propose the Foreground Background Decoupling (FBD) to enhance attention to the foreground areas through random erasure guided by a coarse mask and Incremental PatchExpand (IPg) expands a single image into multiple patches, each guided by different target labels, thus synthesizing a richer variety of instances. Finally, we utilize the detector's loss function as the guiding task loss for dataset condensation. To ensure the quality of the generated images, implement two types of regularization: pixel-level alignment and feature-level alignment. Our method underwent rigorous evaluation on the MS COCO 13 and Pascal VOC 7 6 datasets, achieving top-tier results that highlight its significant effectiveness. With some problems left open and a considerable improvement room existing, we hope this pilot study will attract more community interest.
The primary contributions of this paper can be summarized as follows:
We propose DCOD, the first dataset condensation framework for object detection datasets. Utilizing two-stage process of Fetch and Forge, it can simultaneously condense crucial localization and classification information from the original dataset.
* For the multi-object distribution characteristic of object detection datasets, we propose the Foreground Background Decoupling strategy and Incremental PatchExpand which notably boost the diversity of multi-instances, all within a constrained storage budget.

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(x) Single object syalbelie umages
(b) Muttple-objeet synthetic images
Figure 2: Visualization of images synthesized using DCOD. (a) A synthetic image contains only one category of foreground. (b) A synthetic image contains multiple foregrounds of different sizes, shapes, and categories.
Extensive experiments various detection datasets underscore the superiority of DCOD. Remarkably, even at an extremely low compression rate of 1 %, we achieve 46.4% and 24.5% AP50 the VOC and COCO datasets respectively, significantly reducing the training duration for object detection.
2
Related work
Dataset Condensation. Dataset condensation (or distillation) aims to compress large-scale original data into a small amount synthetic data, accelerating network training while maintaining compa- rable performance. Current research primarily focuses on the field of image classification and can be divided into two frameworks: meta-learning and data matching. the meta-learning framework, methods such DD [27], KIP [7]. RFAD [15], and FRePo [34] update model parameters in the inner loop and synthetic data in the outer loop. the data matching framework. techniques like DC [32), DSA [30], and IDC [9] utilize gradient matching, comparing neural network weight gradients from training on both real and synthetic data. Distribution matching approaches, exemplified by DM [1,,
CAFE [6)]. DataDAM [22]. and IDM (33), align real and synthetic data distributions using Maximum Mean Discrepancy (MMD), often through a single optimization process but potentially limiting the performance. Trajectory matching, such MTT [2], optimizes synthetic data pre-caleulating and storing training trajectories expert networks from the real dataset. A critical downside of MTT is the significant storage requirement for these trajectories.
Recent research suggests compressing original data into models rather than synthetic data. DiM 25 minimizes the difference in predicted logits between real and generated images, using generator to store original dataset information. SR 229 indicates that key dataset information can be preserved in deep neural network training. Inspired by this, we argue that the localization and classification information of the detection data can be learned and preserved by the detector, allowing the key information to be recovered to reconstruct the synthetic image. Unlike SRe2L, we focus on object detection datasets with multiple instances in single image. We propose a boreground-backgreee decoupling strategy and incremental PatchExpand to enhance the updating of synthesized multiple instances.
Diverging from the traditional focus on image classification, our study pioneers dataset condensation in object detection. We introduce a streamlined approach that separates the traditional bi-level optimization into a two-stage Fetch and Forge process. This strategy effectively realizes dataset condensation in object detection tasks with increased efficiency.
High Training Overhead in Object Detection. Object detection methods are broadly divided into two categories: one- stage and two-stage detectors. One-stage detectors, such as YOLO series [19 20), SSD 14). DSFD [2), are valued for their speed and simplicity. Conversely, two-stage detectors like Faster R-CNN [2 prioritize accuracy. Training object detectors, particularly on single GPU, demands substantial time. Training complex model like Faster R-CNN on datasets such as COCO can take several days or even weeks, depending on the configuration and hardware. This extensive training time underscores the pressing need for more efficient training methodologies in object

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Stage-l: Fetch
Detector (
Train
Real Training Images
Stage-Il: Forge
Loc.
Detector
Edet
Imgs
Suppress background
10
Tanqutas
Ryog
Update
foroground
Cls.
Incremental PatchExpand
Fore-back Decopuling
Synthetic Images
Optimization with Loc. and Cls.
Figure 3: Overview of the DCOD framework: In the first stage, Fetch, a detector is trained on the original images, encapsulating key information from the original dataset. In the second stage. Forge, randomly initialized synthetic is enhanced through Foreground Background Decoupling and Incremental PatchExpand initial images, which are then input into the trained detector. Guided by targets, specific category targets are updated in the corresponding areas of the images. The loss of the detector serves as the task loss for condensation, while pixel-level and feature-level regularization ensure the quality of the generated images.
the
detection, highlighting the growing importance of dataset condensation advancements. To address this issue, we introduce the first dataset condensation framework for object detection.
3 Dataset Condensation for Object Detection
3.1 Preliminary
Dataset condensation reduces large real dataset 'T into smaller synthetic dataset S. We first optimization framework to accommodate detection task. For object detection, each image annotated with multiple bounding boxes and their associated class labels. The real dataset T, containing N labeled images, is given by T {X,,, A=) where A, (x My W hgg })) with Kyy My Wyy hy representing the coordinates and sizes the j-th bounding box, and cy being the class label. The synthetic dataset Š, comprising M images where
extend the existing DC [27] 32)
M < N, formulated as S = {(x,, A.) where A, = { Ty ū 2,) represents the synthetic bounding boxes and their corresponding class labels. The optimization process for dataset condensation is twofold:
Model Parameters Update (Inner Loop): The model parameters 0 are optimized over the synthetic dataset. S minimize the loss function T.3(0)) (0):
0 0g(5) arg min 7 (0).
Synthetic Dataset Update (Outer Loop): The synthetic dataset S is refined to minimize the loss the real dataset T using the model parameters OS(S) obtained from the inner loop:
5 = arg min L (8g(5)).
However, this optimization process underlying this procedure involves complex bi-level optimization scheme, which leads to high computational costs. The challenge intensifies when dealing with intricate model structures in the inner-loop or large synthetic datasets in the outer-loop. In the context of object detection, where high-resolution images and complex network structures are the norm, employing such bi-level optimization becomes even more impractical. This emphasizes the need for more streamlined and practical methods dataset condensation, especially for tasks like object detection that involve high computational demands.

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3.2 Fetch and Forge: DCOD Framework
Pioneering the application of dataset condensation object detection, we develop the Dataset Con- densation for Object Detection (DCOD) framework, which is a novel two-stage process depicted in 3 This method diverges from the traditional bi-level optimization that compresses information directly into synthetic data. Instead, we optimize the model and synthetic data separately. Subsequent sections will delve into the optimization objectives for the Fetch and Forge stages.
Stage-I: Fetch. For object detection tasks, the real dataset is given by T = {(X,, A.) with A; encompassing bounding boxes and class labels. By training an object detector the parameterized with A on the original dataset T, we capture key information crucial for the tasks of localization and recognition within images. The optimization can be expressed as:
OT arg min Laa (y2o (X), A):
with Laa representing the composite loss function that combines localization loss Lioe and classifica- tion loss Less:
Lan (aho A) Live (Vo())) A) Las (X), A); minimizing Lde adjusts 0 to detect objects with higher precision.
Stage-II: Forge. In the forge stage, we borrow principles from model inversion [16 28 3], utilizing a well-trained detection model to guide the embedding of essential information back into the synthetic images. To achieve this, is essential recognize the unique aspects of detection tasks: an image typically contains multiple foreground objects with different positions, sizes, and shapes. Therefore, we propose Foreground Background Decoupling (FBD) and Incremental PatchExpand (IPg). Finally, we use the detection loss as the task loss to guide the update of the targets' corresponding areas with the foregrounds of various categories.
Brreground-Backgrowee Decoupling. We initialize the synthetic set S= A))) by randomly selecting a subset of images and their corresponding targets from the original dataset, which includes position coordinates and category labels. The primary objective of object detection is to distinguish the foreground from the background and then classify the foreground objects. Therefore, we prioritize synthesizing the foreground. Additionally, while the background is treated as a single category in detection tasks, we adopt background suppression strategy to avoid blending different contextual semantics (c.g., integrating sky or occan information into a grassy background).
To this end, we propose Foreground-Backer of the foreground and background areas, as follows:
Decoupling (FBD) to separately handle the updating
FBD X [Dback: Ffore 1) 1)= { Tback f- O'Thack (ax <<<) 1)
(5)
Rg((ffr))
We first separate the foreground and background regions using binary mask based on the bounding box coordinates from the original labels. For the background (Zback), we apply a suppression strategy controlled by a hyperparameter a, which limits updates to preserve contextual information. For the foreground (Zfore, we employ a random erasure method Rg, ensuring the model focuses more on refining foreground pixels during the inversion phase. By strategically applying suppression and enhancement, this approach prioritizes important visual information, optimizing the model's ability to generate high-fidelity synthesized images.
Incremental PapanExpand. Unlike classification datasets that contain only one object, images in detection datasets sometimes contain multiple instances of several different categories. Inspired by curriculum learning, we propose Incremental PatchExpand (Ipg) to learn more instances during training. By adopting this approach, we increase the number of divisions, thereby introducing complexity into the synthetic data. We first standardize all images to a uniform size via padding. Subsequently, we employ a procedure, defined by the function:
IPE(X) (X) Expand (Patch(
(Patc(Paadiing( (Padding( (X), A)
(6)
5

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Algorithm Dataset Condensation for Object Detection
Input: Training set 'T
Required: Randomly initialized set of synthetic samples S, initialized detector the parameterized with 0, training iterations K. learning rate 77s
1: Stage-I: Fetch
2: Train tha on T: OJ arg ming (ev A)
3: Stage-Il: Forge
4: for = 0....., K :0....,---.
5: Apply FBD and Er for synthetic set S=FBD(IPE(())
6: Compute task loss Ldet using Equ.
7: Compute Rey using Equ. [ where compute Rpizel and Rfeature using Equ.
gladd Equ. 11
8: Calculate L = Laa( (XX) A) Breg
9: Update S V
10:
end for
Output: Condensed synthetic dataset S
which divides the images into k x l patches and each patch is then optimized for different targets, enhancing the diversity in object position, size, and shape. The incremental introduction more patches allows the model to gradually adapt to various complexities and learn effectively across broader range scenarios.
Optimization with Pixel and Feature Regularization. Following the model inversion and decou- pling framework 28 29 the synthetic images X are optimized by solving the following minimization problem:
argmin Lost (Ve, (8), Rreg
X
where Tater denotes the detection loss and signifies the parameters of the model applied to X. Here, represents the set of annotations for the targets present X. The term Rreg- a regularization term, crucial for preserving the intrinsic qualities of the original dataset within the synthetic images and defined as the sum pixel and feature regularization:
Ryeg Rpiizel + R Rfeatures
(8)
The lixel- regularization R Rpinet comprises two terms:
Resirntt X) = Crr Prv (X) + 04 Ríg (X).
(9)
with Rrv promoting spatial smoothness through the Total Variation regularization:
Rrv(X)= 6,J
(Xes X2.+1)²,
(10)
and Rí, minimizing the squared Euclidean norm of pixel values. The coefficients arv and on, are hyperparameters that balance the Total Variation and Le terms, respectively.
Feature-level regularization Ryeature aims to align the feature statistics of the synthetic image X with the batch normalization (BN) layers of the pre-trained model, as follows:
Rfeature (x) E (1
4i BN + (of{()) o, 112 11).
(11)
where un( () and of represent the mean and variance of the activations the 1-thh BN layer of X. while uBN and of correspond the statistics derived from the pre-trained model. This feature regularization fundamental for the consistency of the feature distribution and the utility of X in training object detectors. At last, summarize the complete two-stage process in Algorithm |

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Table 1: The Performance comparison results Pascal VOC, with the compression ratios 0.5%, 1%, and 2%. The methods compared include Random [8]. Uniform, K-Center (23) and Herding 1 E. The models used in both the compression and evaluation phases are TOLO Ratio (5)): the ratio of con- densed images to the whole training set.
Table 2: The Performance comparison re- sults on MS COCO, with the compression ratios of 0.25%, 0.5%, and 1%. The mod- els used in both the compression and eval- uation phases are YOLOv3-SPP.
Methods
mAP (%)
APro
APms
Methods
(3)
(Ps((%)
(Px(%) AP,())
Ratio (R)
Ratau 0.3 (S)
Kundom
"3302""
15.8.0.
"0260
"[446r
"6766"""
Rindii
333401
9.7=0.T
[6K0.
Uniform
5.0.0
15...0
1550.1
01100.1
1.510.1
6.710.3
Uniform
3.6+0.1
9.80022
1
K-Center
38803
14.520.6
09-0.1
00140.0
0.740.12
5.140.4
K-Center
1.740.1
6.320.0
0.410.0
Hording
36600
12.6.0.2
0.9.0.01
0.100.1
0.9.0.1
4.640.1
Herding
1.740.1
5.8:0.1
0.5+0.1
Ours
14,210.5
37,9.0.9
6.20.4
2440.3
6.630.4
18.140.7
Ours
7.240.1
17,240.2
4.810.2
Rabo 1(%)
Rado (8))
"Kaddf
KBRIS
25.5-0.7
TEG-0
Random
7533000
T422007
Uniform
8.4004
25.7.006
3.2.0.3
0.110.1
3.210.2
128.00.4
Uniform
5.610.1
14.3+0.1
2.910.1
K-Canter
6.1
21,9:099
01:00
1.340.2
8.3+0.3
2.800.0
8.920.1
0.7+0.1
Herding
5.500
19.3-0.5
1.3-0.1
1.340:1
7.940.3
Herding
2.6
8.8+0.1
0.8+0.1
Ours
19,8+0.4
46.420.4
13.020.6
4.440.2
10.5+0.3
24.9:0.5
Ours
10.0+0.1
21.5+0.2
8.0+0.1
Ratio (%)
Rato T1%)
Kandon
15.6005
1446666
Rindom
8.300.0
19.7201
" 5.580.1
Uniform
15.9+0.3
40.6=0.5
8.2=0.5
1.540.3
5.8+0.3
20.620.3
8.440.1
19,740.2
5.4+0.1
Whole Dataset
K-Center
Herding
23.760.6
40.320.5
9.2140.2
8.510.4
28.120.8
31.3
76.420.4
46.720.3
2.6.0.1
2.4.0.1
11.220.6
5640.7
1/410.1
05:0.1
136405
23:240.5
2.110.2
2.5:0.1
32.230.4
29.9.0.7
12.340.2
12.420.3
Center
Herding
Ours
12.1:0.1
4.140.1
4.0+0.1
24.7.0.2
12.5+0.2
12.9+0.1
10.4:0.1
1,3+0,2
1.2+0.1
Whole Dulaset
36.120.2
63.640.3
36.520.2
4 Experiment
4.1
Experiment Setup
The standard evaluation of dataset condensation consists of two steps: first, condensing the original training set into smaller synthetic dataset; then, training an initialized model with this synthetic data and evaluating it on the original dataset's 's test set to assess the synthetic data's effectiveness in model training.
Datasets and Metrics. Our DCOD method is evaluated on Pascal VOC 7 6 and MS COCO 33 benchmarks, with image resolution set to 512x512. For Pascal VOC, we merge the trainval sets of VOC2007 and VOC2012 into single training set with 16,551 images, using the VOC2007 test set for evaluation. MS COCO comprises 80 categories with 1 118,287 training and 5,000 test images. Both datasets are assessed using standard COCO metrics: mAP (mean Average Precision), AP (0.5 loU threshold), AP75 (0.75 IoU threshold), and size-specific AP,, AP,,, AP; for performance evaluation. We initialize the network with pretrained backbone weights and train it 5 times on the distilled dataset. The evaluation is conducted on the original test dataset to obtain the std.
Implementation Details. The initialization of the synthetic set involves random sampling from the original images, adhering to the specified compression ratio. The compression rate in classification tasks is typically set to around 1%. In our task, for VOC, we evaluate compression rates of 0.5%, 1%, and 2%, while for COCO, we report at 0.25%, 0.5%, and 1%. During the standard image synthesis process, we set the image learning rate 0.002. The weight for the task loss is set at 1, while Reeature is assigned a weight of 0.1. The aTV and 0z2 is established at and 0.001, respectively. The a in background suppression strategy is set 0.7. All experiments were performed on a single V100 gpu.
4.2 Experimental Results
Counterpart Methods. As we are the first dataset condensation methods for object detection, we refer to the comparison settings of general DC in classification (27 32) and choose three core-set selection methods: Random [18], K-center 233, and Herding [ 4. The Random method randomly selects real images from the original training set to form a subset. To adapt the K-center and Herding for object detection tasks, we implement a straightforward modification by using a pre-trained feature extractor (ResNet50) to process the features of entire images and employing the L.2 norm to measure distances. Additionally, considering class balance, we also employ the Uniform, which is commonly used in practical applications within the field of object detection.
7

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Table 4: Cross-archilecture performance Pascal VOC MS COCO. We use one-stage detector YOLOv3-
Table 3: Ablation Study on the components of Forge, FBD, and Ipg 1% compression rate Pascal VOC, with model using YOLOv3-SPP.
SPP and the two-stage detector Faster RCNN for the evaluation phase, respectively.
Ratio (%)
Methods
mAP(%)
(%)
(%)
Ratio
mAP(SS)
VOC
AP
MAP(X)
COCO
APwi((]
baseline
8.340.6
25.940.5
2.910.3
0.5 YOLO
14.210.5
37.910.9
10.010.1
21.5102
haseline+/'pp
10.0+0.3
27,940.4
3.40.22
Faste-RCNN
6,3404
18.540.7
3740.3
84402
baseline+Ipp
17.7=0.4
42.620.5
11.6=0.4
YOLO<3-SPP
1988004
46.430.4
12.120.1
24.7:022
baseline+ ppp
19.8+0.4
46.4+0.4
13.0=0.6
Fastr-RCNN
138+0.5
33.240.6
6.1+04
13,540.3
Table 5: Comparison of different initialization on Pascal VOC with compression ratio of 0.5% using YOLOv3- SPP.
Initial Methods
mAP( %6)
AP (%)
Noise
6,910.7
23.240.5
Figure Ablation of arV on Pascal VOC, with a compression ratio of 1%, using YOLOxv-SPP.
Random
14.240.5
37.940.9
K-center
11.9+0.6
34.2+0.4
Herding
11.5+0.7
33.820.6
Comparison Results. Table T and 2 provide the comparative results of our dataset condensation method on the VOC and COCO datasets, underlining its superior performance all compression rates. According to the official ultralytics/yolov3 implementation, the detector achieves an mAP of 46.5% and A0 of 76.4% on VOC when trained with the full dataset, and 35.6% mAP on COCO, which serves as approximate upper limit of performance. On VOC, using 2% compression rate, our method still reaches an AP of 50.7%. On COCO, a %% compression rate, the mAP and AP30 reach 12. % and 24.7% respectively. Compared to random sampling, at the lowest compression rate 0.5% on VOC, our method increases mAP and AP by 9.3% and 22. 1% respectively, and at COCO's lowest compression rate of 0.25%, our method improves mAP and AP by 3.7% and 7.5% respectively. Moreover, while the Uniform method shows slight improvement over Random, it still falls short of the performance achieved by approach. The Coreset method, focusing only on the overall image features and neglecting instance-level details, shows poor performance. Our method also demonstrates comprchensive advantages in multi-size object detection metrics like AP.. APm. AP, indicating that our DCOD significantly enhances the diversity foreground positions, sizes, and shapes.
4.3 Ablation Study
Effectiveness of Each Component. Table 3 shows that using model Inversion from Forge as our baseline, our proposed augmentations IPE and FBD improve performance by 1.7% and 9.4% respectively. Combined, they yield an 11.5% overall improvement. Notably, the baseline alone underperforms compared to random sampling. We found that without these augmentations, the detection loss quickly converges in early iterations, missing key positional and classification details. Thus, incorporating both Ipg and FBD is crucial to enhance the diversity and effectiveness of the synthetic images.
Cross-architecture Generalization. We verify the performance of condensed data on new archi- tectures, and in Table 4 our approach is able maintain good generalization at 1 % compression. However, we observe a larger performance drop at lower compression rates. This is due to the fact that the one-stage detector and two-stage detector architectures differ significantly, preserving limited information when too few images are synthesized.
Sensitivity of Hyper-Paramnetrs. Figure shows how different Qrv regularization weights affect our method's performance, using the VOC dataset with a 1% compression rate. The results indicate that an optimal regularization weight enhances performance, while no regularization or too much regularization reduces it. This happens because lack of regularization introduces excessive noise, and too much regularization causes excessive smoothing the synthetic images, both of which degrade the quality of the images.

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GT
Iter
Iter 500
Iter 1000
Iter 2000
Iter 3000
RORA
Figure 5: Visualization of different iteration steps during the condensed phase. Scores are assigned using the trained YOLOv3-spp model, based on IOU@0.5. The "GT" column represents the true labels of images. "iter0" shows the initial image scores, followed by scores of synthetic images and their bounding box iterations 500, 1000, 2000, and 3000.
Figure 6: Visualization of the multi-instance synthesized images. Scores are assigned using the trained YOLOv3- SPP model, based TOU@0.5.
we compare the performance variations of the Random baseline method and the DCOD method as the compression ratio increases. The performance of the full dataset, marked by gray line, serves as the theoretical upper bound. When the ratio below 5%, DCOD shows a significant advantage over the random method, while as the ratio exceeds 20%, the performance of both methods converge.
Performance upper bound As shown in Figure 7
Discussion Initialization We discuss the impact of initialization sampling the performance of the synthetic dataset this section. As shown in Table 5) the Random method, which is the primary choice for the experiments in this paper, is simple and performs well. Initialization strategies based on K-center and Herding lead to decline in performance. This is these core-set methods fail account for the complex factors in detection datasets, such class distribution and object size distribution, resulting in significant differences between the synthetic and original datasets. Using noise the initialization method, due the absence of any prior information from the original dataset, causes a notable drop in the effectiveness of the synthetic dataset.
4.4 Visualization
present a comparative analysis of synthetic images across various iterations, as shown in Figure 5. Notably, there is a progressive enhancement in the foreground scores with advancing iterations, indicative of the incremental integration of category-specifie beneficial information into the images. Specifically, for sheep in the first row, the scores at iter500 and iter 1000 start lower than the initial

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Figure 7: Visualization of performance Pascal VOC using YOLOv3-SPP the compression ratio
increases.
image but surpass initial scores after iter2000. This trend implies that early images are initially disrupted by noise from the update process, but as iterations progress, pixel, and feature regularization help maintain image clarity.
Additionally, also showcase the visualization results of synthetic images containing multiple instances, as shown in Figure 6, demonstrating that DCOD effectively supports the generation of multiple instances required in detection tasks.
Conclusion, Limitations and Future Work
In this study, we introduce the first dataset condensation framework for object detection, DCOD. It is a two-stage framework where, in the first stage, Fetch stores information vital for detection from the original dataset into model parameters. In the second stage, Forge, the images then reconstructed through model Inversion. We propose foreground background decoupling and incremental Patch- Expand, to improve the efficiency and diversity of the synthetic images. Extensive experiments are conducted on two standard detection benchmarks, Pascal VOC and MS COCO, to demonstrate that DCOD significantly maintain superior performance
extremely low compression rate.
We acknowledge the limitations of our work from two perspectives. First, due the significant differences among object detectors, we did not extend our approach to more complex detectors like DETR, as this might require more specialized designs to accommodate their structures. Second, the performance across different architectures is still insufficient and remains an area that needs improvement.
Acknowledgments and Disclosure of Funding
This work was supported by National Natural Science Fund of China (62076184 62473286. 62306046) in part by Shanghai Natural Science Foundation (227R1466700) 4667000)
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Reviews:
[{"review_id": "RsLE83lyt8", "reviewer": "Reviewer_z3Af", "summary": "This manuscript introduces, as far as I’m aware, the first method to do object detection dataset condensation. The method accomplishes this by first training a detection model on the original dataset called the Fetch stage, then uses this trained model to synthesize a condensed dataset through model inversion and regularization, called the Forge stage. Two modules are introduced to aid in inversion: Forward Background Decoupling (FBD) and Incremental PatchExpand (IPE). FBD induces separate processing on background and foreground pixels; background pixels are down weighted and foreground pixels are randomly masked. IPE progressively divides images into smaller and smaller patches as training progresses and processes different targets in each of these patches. A total variation regularizer is used to ensure synthesized image smoothness and a batch norm feature regularizer is used to ensure the batch norm mean and variance statistics are similar between real and synthetic images. Results on generated datasets outperforms random subsamples of the larger real dataset.", "questions": "This manuscript is interesting and if the authors address the weaknesses I’ve listed, I’ll be happy to raise my score from a 4 to a 5 or above. The most involved item I’d like to see is W1. This should hopefully be a relatively straightforward calculation followed by evaluation on already trained models. W2-8 are items that need to be added to the manuscript to ensure the method can be reproducible given the manuscript text.\n\nAdditionally, I’ve provided additional comments on how to improve the manuscript at the end of the weaknesses section, but these have not factored into my score.", "limitations": "The manuscript has addressed any concerns I’d have regarding limitations.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "*S1:* As far as I’m aware, this is the first work on dataset condensation for object detection and appears to be performant.\n\n*S2:* Aside from the missing definitions I’ve mentioned in the weaknesses section, this manuscript was clear and well written.", "weaknesses": "**Weaknesses:**\n\n*W1:* The distribution of classes varies quite a bit at small/medium/large object sizes for both VOC and MS-COCO. It’s possible that K-Center and/or Herding sampling strategies conform to this distribution, but if not, I expect that this would be a strong baseline and should be compared against.\n\n*W2:* In line 159, it states “the foreground is derived from the bounding box coordinates of the current targets”. This needs to be more precise. How exactly is it derived? Is a union taken over all bounding boxes of the current batch and those pixels used as the foreground?\n\n*W3:* In line 167, it states “inspired by course learning”. Is this meant to be curriculum learning? \n\n*W4:* “Expand” in Eq. 6 is never defined. Are image patches upsampled be the same size as the original image? \n\n*W5:* The rate of increase for $k$ in the IPE module is never provided. \n\n*W6:* Does $\\tilde{A}$ stay the same as the images sampled at initialization to run synthesis on? If so, how are these samples selected? This seems like it would drastically affect performance.\n\n*W7:* line 218: What is “the Uniform”? I have worked in object detection for years and this term isn’t something I recognize. A citation here is needed.\n\n*W8:* Specify how error bars were computed. Are these the standard deviation over multiple runs of dataset condensation?\n\n\n\n**Additional comments, not affecting my rating:**\n\n*C1:* The description of object detectors in line 99-102 isn’t quite correct. Fast models here are the YOLO series and SSD models. These are fast because they don’t include additional processing for small objects, e.g., an FPN. Faster R-CNN and RetinaNet are more accurate because they include an FPN. \n\n*C2:* On line 222: what open-source implementation is used? This should have a citation.\n\n*C3:* Line 230: “The Coreset method” I assume this is referring to K-Centers, but terminology should stay consistent.\n\n*C4:* It would be interesting to compare dataset condensations across more similar architectures. For instance, it should be easy to train a dataset condensed on RetinaNet or Faster-RCNN with FCOS, ATSS, or TOOD.\n\n*C5:* It would be interesting to see Figure 5 on multi-class images.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "### Q1 [Analyze the class distribution of small/medium/large objects in the K-Center and Herding.]\nBased on our analysis, we compare the distribution of small(area<32x32), medium, and large(area>96x96) objects sampled by the k-center and herding methods with the original dataset, presenting the results as stacked bar charts. Please refer to Figure 1&2 in the supplementary PDF. In Figure 1, for the VOC dataset, we observe that k-center tends to select larger images, while herding includes both medium and large images, with both methods selecting fewer small-sized objects. In Figure 2, for the COCO dataset, K-center's selection is more aligned with COCO's inherent proportions, though some categories show significant differences. This may be due to COCO's more balanced object size distribution. These sampling discrepancies might contribute to the poor performance of the methods.\nSubsequently, we design a new baseline approach, called 'Size-balance':\n1. Calculate the frequency of occurrence for each category based on the sampled subset's size.\n2. For each category, sample the corresponding number of small, medium, and large samples according to the original dataset's proportion.\nFinally, we compare this baseline sampling approach with subsets from VOC 1%, and COCO 1%. The results outperform the random baseline; however, our method still surpasses this new baseline. A more comprehensive comparison table will be provided in the revised submission.\n| Sample ratio |     Methods   |      mAP      |     AP50     |\n|-------------| --------------|-------------- | ------------ |\n|    1% voc    | K-center      |    6.1±0.2    |    21.9±0.9  |\n|    1% voc    | Herding       |    5.5±0.2    |    19.3±0.5  |\n|    1% voc    | Size-balance  |    9.1±0.3    |    26.7±0.5  |\n|    1% coco   | K-center      |    4.0±0.1    |    12.9±0.1  |\n|    1% coco   | Herding       |    4.1±0.2    |    12.5±0.2  |\n|    1% coco   | Size-balance  |    8.7±0.2    |    20.3±0.4  |\n### Q2 [How foreground be derived?]\nThe foreground coordinates are sampled from the real dataset annotations. During the initialization phase, we random sample the images and their corresponding targets from the dataset, and the bounding box coordinates provided in the dataset annotations are used to define the foreground regions. \n### Q3 [course learning->curriculum learning?]\nYes, the term \"course learning\" was intended to refer to \"curriculum learning.\" We appreciate your feedback and will make the necessary correction in the revised manuscript. Thank you for pointing this out.\n### Q4 [Expand: Are image patches upsampled be the same size as the original image?]\n\"Expand\" in Equation 6 refers to the process during the train/test pipeline where synthestic image are upsampled to a specified size, typically 512x512. We will clarify this in the revised manuscript.\n### Q5 [The rate of k increase in IPE.]\nThe growth setting for k is straightforward; we linearly increase k based on iterations. Specifically, k increases from 1 -> 2 -> 3, with an increment every 1000 iterations. \n### Q6 [How does $\\widetilde{A}$ be smapled?]\nYes, $\\widetilde{A}$ corresponds to the images sampled during initialization. Since we use random sampling, the performance of the synthesized dataset may experience slight variations due to different initializations. We reported this standard deviation in Experiments.\nAdditionally, we have supplemented the experiments as follows:the performance of the synthesized dataset on 0.5% VOC when using other sampling methods as initialization, including the new baseline 'size balance' established in W1. The study of initialization strategies could be a promising direction for future research.\n| intial method |       mAP      |     AP50     |\n| ------------- | -------------- | ------------ |\n|    noise      |    6.9±0.7     |   23.2±0.5   |\n|    random     |    14.2±0.5    |   37.9±0.9   | \n|    k-center   |    11.9±0.6    |   34.2±0.4   | \n|    herding    |    11.5±0.7    |   33.8±0.6   |\n| Size-balance  |    14.8±0.4    |   38.5±0.3   |\n### Q7 [ What is 'the Uniform'?]\nThe 'Uniform' refers to uniform class sampling, meaning that each class appears approximately the same number of times. We will add appropriate annotations in the tables to clarify this term. \n### Q8 [Are these the standard deviation over multiple runs of dataset condensation?]\nYes, the error bars represent the standard deviation over multiple runs of dataset condensation. Following the standard dataset condensation training and testing pipeline, we performed dataset condensation five times. For each generated synthetic set, we conducted five testing runs, resulting in a total of 25 results. The standard deviation was then calculated based on these 25 outcomes.\n### Q9 [The description in line 99-102 isn't quite correct.]\nThank you for your observation. We will revise the description of object detectors in related works.\n### Q10 [what open-source implementation is used?]\nWe follow this open-source implementation:official ultralytics/yolov3\n### Q11 ['The Coreset method',terminology should stay consistent]\nIn lines 213-214, we mentioned that core-set selection methods include random, K-Center, and herding. We will emphasize this point again in line 230 and ensure consistent terminology throughout the paper. Thank you for your suggestion.\n### Q12 [Train a dataset condensed on other detector.]\nWe agree with the suggestion to deploy more detectors for condensing; however, due to time constraints and the significant differences in environments and configurations of different detectors, extensive code modifications are required. We plan to include these detectors in future versions.\nAdditionally, we will consider applying this framework to other tasks, such as segmentation, in future work.\n### Q13 [It would be interesting to see Figure 5 on multi-class images.]\nWe have added visualizations of multi-class images. Please refer to Figure 4 in the supplementary PDF for details.", "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": "MAYdll1EqM", "reviewer": "Reviewer_8mRE", "summary": "The paper looks at the task of dataset condensation for object detection. While there have been many works looking at dataset condensation for classification this is the first work to look at it for detection which is more challenging as each image can contain multiple objects of different categories. This is an important problem as it reduces the training requirements. The proposed method operated in two stages, first training a model on the full data. Followed by model inversion to get synthetic images which capture the most important classes and regions. The authors also propose new techniques to handle the multi object nature of images and show good results on VOC and COCO.", "questions": "I think the paper is well written and in general easy to follow and sound. At the same time I have mentioned a few clarification and discussions in the weaknesses and would hope the authors answer some of them. Especially around background, initializations and upper bound with more images. Overall I feel the problem is important and the authors propose a first step towards solving it.", "limitations": "Yes.", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper has the following strengths:\n1. The paper looks at an important and challenging problem of dataset condensation for object detection. I have seen many works look at data distillation for classification but this is more challenging and novel task to look at.\n2. The authors provide a clear way to propose the first method for this task. They remove the need for expensive 2 step approaches which include meta learning or feature/data matching and rather simplify the problem with the proposed fetch and forge approach.\n3. The authors show results on common datasets like VOC and COCO and while they cannot match the full data performance I think it might be a good first step.\n4. The authors do ablation of various components, hyperparams and generalization and also show qualitative results.", "weaknesses": "The paper has the following weaknesses:\n\n1. In object detection and recognition multiple works have show that the context (background) plays an important role. [Noise or Signal: The Role of Image Backgrounds in Object Recognition]. The Fetch a Forge paper continuously suppresses background (L155-1156) and considers background not important. Is there backing of these claims and why would context (background) not help?\n2. Lacking discussion about the input initialization. What effect do different input initializations have. Is there a way to find an optimal initialization? I think the initialization part needs to be discussed in more detail.\n3. Section 3.1 seems less relevant given the paper is talking about object detection not classification. It should be modified to describe the setup in terms of detection data.\n4. Missing discussion of what happens with different initializations during the Fetch part. Do you get similar performance and visualization of the condensed data?\n5. Missing analysis of whether during condensation for object detection location is more important or classification while performing inversion to get the condensed synthetic samples.\n6. The authors should also report at what percent of condensed data they can meet the real number. That is important to get some idea of compression to number of images tradeoff and what the best performance this method can give.\n\nSmall clarifications:\n1. Its confusing why incremental patch expand is needed if the network is already trained during fetch step.\n2. Missing ablation regarding the Reg loss in Eq 7 and also within it the importance of pixel vs feature regularization.\nL112 For the complex of -> For the complex case/scenario\nL167 course -> coarse", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "### Q1 [Background suppression and its impact on object detection.]\nWe acknowledge the importance of background in detection tasks but prioritize updating the foreground in image synthesis while retaining the background for better results. \"Suppress background\" means limiting updates to background pixel values to preserve contextual integrity. For example, a background originally containing the sky could be mixed with information like the ocean or grass, disrupting the original context. We will clarify that background is not \"unimportant\" and add a discussion on this topic. Our ablation study on 1% VOC shows that increasing background update weight decreases performance, confirming our suppression strategy.\n|fore_weights|back_weights|mAP|AP50|\n|-|-|-|-|\n|1|1|18.6±0.8|44.1±0.7|\n|1|0.7|19.8±0.4|46.4±0.4| \n|1|0.5|19.6±0.5|46.1±0.3| \n|1|1.5|18.1±0.5|43.5±0.8|\n|1|2|17.7±0.3|42.9±0.7|\n### Q2 [Discussion about the input initialization.]\nIn this paper, we randomly sampled images and labels from the real dataset for initialization, leading to variations in the synthetic set's foreground categories and object size distribution. This caused result fluctuations. Defining a good initialization for detection dataset condensation is complex, considering factors like category frequency and bounding box characteristics. We supplemented experiments on VOC 0.5% with different methods, including noise, random, k-center, herding, and a 'size-balance' method suggested by Reviewer #z3Af.\n\n|intial method|mAP|AP50|\n|-|-|-|\n|noise|6.9±0.7|23.2±0.5|\n|random|14.2±0.5|37.9±0.9|\n|k-center|11.9±0.6|34.2±0.4|\n|herding|11.5±0.7|33.8±0.6|\n|Size-balance|14.8±0.4|38.5±0.3|\n\nNoise performed the worst, consistent with experiences in classification dataset condensation. Random and Size-balance achieved good results. Future studies on initialization in the detection domain could be key to improving condensation performance. We will continue exploring how to select representative samples to enhance efficiency and effectiveness.\n### Q3 [Section 3.1 should be more on detection data]\nThank you for your suggestion. \nWe will revise Section 3.1 to include a detailed description of the setup for the detection dataset condensation task\n### Q4 [what happens with different initializations during the Fetch part.]\nThe Fetch phase involves standard training of detectors, with initialization referring to the detector's initial weights. The weights obtained after Fetch training are also used as initial model weights for the Forge phase, influencing the quality of synthesized images. We tested models trained for different numbers of epochs for inversion, leading to varying performances, as shown in the table below for 0.5% VOC:\n\n|training epoch|mAP|AP50|\n|-|-|-|\n|100|14.1±0.3|37.5±0.5|\n|300|14.2±0.5|37.9±0.9|\n|500|13.8±0.6|37.1±0.6|\n|1000|11.4±0.5|33.1±0.7|\n\nProlonged Fetch phase training can negatively affect synthesized image quality, possibly due to excessive data compression, complicating reconstruction. This issue needs further investigation in future work.\n### Q5 [Importance of location vs. classification in condensation]\nWe conducted a new experiment to address this concern by adjusting the weights of the classification and location losses during the inversion process. For YOLOv3-SPP, we used GIoU loss for location and CrossEntropyLoss for classification. The results are shown in the table below with a 0.5% compression ratio on VOC.\n\n|CEloss|GIoUloss|mAP|AP50|\n|-|-|-|-|\n|1|1|19.8±0.4|46.4±0.4|\n|1|1.5|19.7±0.6|46.2±0.5|\n|1|2|19.3±0.3|45.9±0.4|\n|1|0.5|19.2±0.4|45.5±0.7|\n|1|0.2|18.3±0.8|44.7±0.6|\n\nWe found that a 1:1 weight ratio is very suitable. When the weight for the localization loss is further reduced, the performance metrics show a noticeable decline.\n### Q6 [Report percentage of condensed data meetting real data.]\nDue to time constraints, we increase the compression ratio on the voc as follows:\n\n|VOCRate|Methods|mAP|AP50|\n|-|-|-|-|\n|5%|Random|27.9|58.4|\n|5%|Ours|30.5|62.3|\n|10%|Random|35.1|65.1|\n|10%|Ours|36.3|66.2|\n|20%|Random|39.3|69.4|\n|20%|Ours|39.9|70.1|\n|50%|Random|41.5|72.6|\n|50%|Ours|41.7|72.8|\n\nWe find that as the ratio increases, the advantage of the condensation method compared to the baseline gradually decreases, which is consistent with some studies in classification([DC-BENCH: Dataset Condensation Benchmark]). In future work, we will further explore how to achieve lossless methods.\n### Q7 [why IPE is needed if the network is already trained]\nIncremental Patch Expand is used in the Forge phase to increase the diversity of the synthetic images. Taking into account the complexity of multi-instance detection task, we introduced the incremental approach to gradually increase the number of instances being optimized.\n### Q8 [pixel vs feature regularization]\nWe have add an ablation study on pixel versus feature regularization on 1%ratio VOC, as shown in the table below:\n\n|Rpixel|Rfeature|mAP|AP50|\n|-|-|-|-|\n|√|√|19.8±0.4|46.4±0.4|\n|×|√|17.8±0.5|43.2±0.3|\n|√|×|1.8±0.2|3.5±0.5|\n|×|×|0.3±0.0|0.8±0.1|\n\nFor inversion techniques, Rfeature is crucial for reconstructing the information content in the synthesized images. Rpixel helps maintain performance to some extent by balancing the image pixel distribution and reducing sharp regions in the pixels.", "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": "mwpR35tVqc", "reviewer": "Reviewer_8kYh", "summary": "The paper attempts to generalize dataset condensation to object detection. It proposes Fetch and Forge. Fetch: training a normal object detector on the original dataset as usual. Forge: the synthetic images are sampled from the original data and optimized by the detection loss. Experiments on VOC and COCO show the effectiveness of the method.", "questions": "see weaknesses above", "limitations": "see weaknesses above", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is well-motivated and easy to understand. \n\n2. The proposed methods are technically sound.\n\n3. The experiments show promising results and there is much room for future.", "weaknesses": "1. Notation issue: see $\\mathcal{I}_{PE}$ in Eqn. 6 and Step 5 of Alg.1.\n\n2. Why is row 3 of Table 3 blank?\n\n3. Implementation Details are not comprehensive. Is it the training epoch for IPE set to $M$/batchsize? And I would suggest not calling it a training epoch.\n\n4. What would happen if we increase the condensation ratio, e.g., 5%, 10%, 20%, 50%?\n\n5. Typos: it should be ''ratio'' in Table 3.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "### Q1 [Notation issue: see IPE in Eqn. 6 and Step 5 of Alg.1.]\nWe note the inconsistency in the notation for I_PE between Equation 6 and Step 5 of Algorithm 1. We will correct this inconsistency in the revised version of the paper. Thank you for bringing this to our attention.\n### Q2 [Why is row 3 of Table 3 blank?]\nThe blank row in Table 3 was due to a LaTeX formatting error. It should have displayed the results for the \"baseline+IPE\" method. We will correct this issue in the revised version of the paper. Thank you for pointing it out.\n### Q3 [Implementation Details are not comprehensive. Is it the training epoch for IPE set to M/batchsize? And I would suggest not calling it a training epoch.]\nYes, we set M/batchsize for IPE. We will correct the misleading terminology by using \"iterations\" instead of \"epochs.\" Thank you for the suggestion, and we will include more detailed implementation details in the revised version.\n### Q4 [What would happen if we increase the condensation ratio, e.g., 5%, 10%, 20%, 50%?]\nBased on your suggestion, we add experiments on VOC with compression ratios of 5%, 10%, 20%, and 50%. See tabel:\n\n|  VOC Rate |   Methods    |    mAP      |     AP50    |\n|-----------| ------------ | ----------- | ----------- |\n|      5%   |    Random    |    27.9     |     58.4    |\n|      5%   |    Ours      |    30.5     |     62.3    |\n|     10%   |    Random    |    35.1     |     65.1    |\n|     10%   |    Ours      |    36.3     |     66.2    | \n|     20%   |    Random    |    39.3     |     69.4    |\n|     20%   |    Ours      |    39.9     |     70.1    |\n|     50%   |    Random    |    41.5     |     72.6    |\n|     50%   |    Ours      |    41.7     |     72.8    | \n\nWe find that as the ratio increases, the advantage of the condensation method compared to the baseline gradually decreases, which is consistent with some studies in classification([DC-BENCH: Dataset Condensation Benchmark]). In future work, exploring how to train using only synthetic images to surpass the performance upper bound of real images is a very interesting topic.\n### Q5 [it should be ''ratio'' in Table 3.]\nThank you for pointing out the spelling error. \"Ratio\" refers to the compression ratio of the entire dataset. We will correct this in the revised version of the paper.", "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": "DsI3pRwObx", "reviewer": "Reviewer_SZRX", "summary": "1) The paper introduces a dataset compression technique and applies it for the first time in object detection tasks.\n 2) For large-scale object detection tasks, the condensation of datasets can not only shorten model training time but also save a lot of computational resources.\n3) The method proposed in the paper is simple and effective, which can effectively solve the problems raised.", "questions": "1、In the experimental section, it is possible to add several more datasets for comparison of condensing methods.\n2、Why is the author's two-stage model framework more effective than a complex bidirectional compression optimization framework? Please discuss in detail.\n3、It cannot be seen from Figure 3 what the target in the annotation refers to.\n4、Formula 5's introduction is not specific enough.\n5、How is each patch optimized for different goals and increased complexity in the IPE module?\n6、Some annotations are missing in Tables 1 and 2, such as how the results of the entire dataset are obtained. What does \"±\" mean?\n7、Based solely on the experimental results, due to the lack of comparison with similar experiments, the experimental results lack some persuasiveness.\n8、How is the synthesized image updated under the guidance of loss?Suggest adding composite images with multiple instances in the visualization result graph.\n9、Lack of reference to Figure 5 in the visualization section.", "limitations": "1.Due to the significant differences among object detectors, you did not extend your approach to more complex detectors like\n DETR, as this might require more specialized designs to accommodate their structures. \n2. The performance across different architectures is still insufficient and remains an area that needs improvement.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "DCOD is distinct in its ability to flexibly generate foreground objects of varying sizes, shapes, and categories at any position within an image. Additionally, DCOD streamlines the process by eliminating the complexities of traditional bi-level optimization, enhancing \n its compatibility with complex detection networks and large-scale, high-resolution data.", "weaknesses": "1.Algorithm needs to be optimized.\n2.The description of the second stage of DCOC is not clear enough.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "### Q1 [Algorithm needs to be optimized]\nIn this work, we analyze why the traditional bidirectional framework is challenging for detection tasks (lines 35-40). We propose the fetch and forge two-phase framework, which avoids the computational cost of second-order derivatives and achieves critical optimization for detection tasks. Due to the complexity of detection tasks, further detailed optimizations will be explored in future research.\n### Q2 [The description of the second stage is not clear enough]\nFor unclear areas, please refer to the specific responses provided in the following questions. We will include more details in the revised version.\n### Q3 [Add several more datasets.]\nWe understand the reviewer's concerns and will consider adding larger-scale general detection datasets, such as Object-365. However, due to time constraints, completing these experiments in the short term is challenging. If the paper is accepted, we will include additional detection datasets in the revised version.\nMoreover, Pascal VOC and MS-COCO are the two most influential benchmark datasets in the object detection field. As far as we know, many well-known detection papers have also only used these two datasets. We have demonstrated the effectiveness of our method on these datasets.\n### Q4 [Discuss about two-stage framework vs bidirectional framework ]\nThe bidirectional method optimizes outer image updates by comparing performance differences between real and synthetic sets, using gradients or model parameters to compute matching loss. The model also requires inner loop optimization involving gradient calculations, resulting in second-order derivatives and high computational costs. As image resolution and model complexity increase, the efficiency of second-order derivative calculations decreases, making outer loop optimization for synthetic images challenging. As stated in lines 32-35, these methods are only effective on small datasets and networks.\nOur two-stage method decouples the original second-order calculations, allowing separate training of a complex detector and optimization of high-resolution images (lines 43-45). This separation improves efficiency.\n### Q5 [Figure 3 lack target.]\nThank you for pointing that out. The term \"target\" refers to the objects randomly sampled along with the images during initialization. We will supplement the targets in Figure 3 in the revised version.\n### Q6 [Add formula 5's introduction.]\nWe are pleased to provide a more detailed explanation:\nIn synthesizing the image $\\widetilde{X}$, we separate the foreground and background regions using a binary mask. For the background ($x_{\\text{back}}$), we apply a suppression strategy with a hyperparameter $\\alpha = 0.7$, limiting updates to preserve contextual meaning. For the foreground ($x_{\\text{fore}}$), we employ a random erasure method, ensuring that the model focuses more on refining the foreground pixels during the inversion phase. By strategically applying suppression and enhancement, we prioritize the processing of important visual information, optimizing the model's ability to generate high-fidelity synthesized images.\n### Q7 [Patch optimization and goal complexity in IPE module.]\nWe employ an incremental approach to enhance the model's adaptability to an increasing number of ground truth labels and bounding boxes. Initially, we introduce a modest number of gt labels and bboxes, allowing the model to assimilate the initial dataset without overwhelming its learning capacity. Our methodology progresses through three stages, each comprising 1000 iterations. Within each stage, we incrementally increase the instance count, denoted by k, starting from 1*1 and progressing to 2*2, and finally to 3*3. This curated incrementation ensures a robust and sustainable learning trajectory.\n### Q8 [Missing annotations in Table1&2 and \"±\" explanation. ]\nThank you for pointing out this issue. The performance on the full dataset is based on the open-source implementation from official ultralytics/yolov3. The \"±\" symbol represents the standard deviation calculated from 25 results, obtained by generating five synthetic sets and testing each set five times.\n### Q9 [Lack of comparison with similar methods]\nTo our knowledge, our work is the first to study dataset condensation for object detection. Traditional methods for dataset condensation in classification are difficult to apply to detection tasks, making direct comparisons challenging.\nHowever, we understand the reviewer's concerns and are considering adding a discussion section to analyze the difficulties and challenges of adapting classification dataset condensation pipelines to detection tasks. This discussion could inspire future research in this area.\nIn fact, early papers on classification dataset condensation (such as [Dataset Distillation] and [Dataset Meta-Learning from Kernel Ridge-Regression]) only compared against random and core-set methods. Therefore, we are essentially following their experimental framework.\n### Q10 [How is the synthesized image updated under the guidance of loss? add multiple instances in the visualization.]\nPlease refer to Figure 3 in the supplementary PDF, where we show the variation in guidance loss over 3000 iterations and the test performance of synthetic images(tested every 100 iterations).\nWe also add more visualization for multi-instance in Figure 4 of the supplementary PDF.\n### Q11 [Lack of reference to Figure 5.]\nApologies for the oversight. We will add a reference to Figure 5 in the revised paper. Thank you for pointing it out.\n### Q12 [ Limitation ]\nThe two limitations are actually discussed in the \"Future Work\" section of our paper.\n1.We will investigate more complex detector architectures following the inversion approach.\n2.We plan to build a model pool of different detectors during the fetch phase and perform inversion using multiple detector weights on the synthestic image in the forge phase to incorporate diverse information.", "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:
We are grateful to all reviewers for acknowledging our work and providing valuable comments and suggestions.
Common strengths noted:
1.Recognition of our motivation and contributions as pioneers in studying detection dataset condensation.
2.Demonstrated effectiveness of our method on two widely-used benchmarks, VOC and COCO, underscoring the potential for significant advancements in this domain.
3.Clear and comprehensible writing.
Common question noted:
1.Need for further research on performance limits with increased compression ratios.
2.Further investigation required on the initialization issue.

Additionally, in the supplementary PDF, Figure 1 and Figure 2 address Reviewer z3Af's Q1 regarding the baseline sampling strategy and the distribution of small/medium/large sizes. Figure 3 addresses Reviewer SZRX's Q8 on how task loss guides synthetic images updates. Figure 4 addresses the multi-class multi-instance visualization raised by Reviewer z3Af and Reviewer SZRX. Due to time constraints, the standard deviation in all supplementary experiments was obtained by generating twice and testing three times each. 

Finally, we commit to addressing all concerns and incorporating the mentioned experiments in the revised manuscript if the paper is accepted.

Meta-review: The paper addresses the dataset condensation task for object detection, a more challenging problem than classification. It introduces a two-stage method, Fetch+Forge, with a clear description and thorough experimental evaluation. Reviewers generally agree this is a novel approach, though concerns about writing clarity and evaluation quality were noted. The final ratings varied from Borderline Accept to Accept. The meta-review confirms that most issues raised have been resolved, particularly for reviewer SZRX. Given the clear method and significant results, the AC recommends accepting the paper.
Decision: Accept (poster)