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Detection Results: VOC2010 BETA
Competition "comp3" (train on VOC2010 data)
This leaderboard shows only those submissions that have been marked as public, and so the displayed rankings should not be considered as definitive.- The highest scoring entry in each column is shown in bold.
- Clicking on the blue arrow symbol (
) at the top of a column will order
the submissions from high to low wrt performance on that column. - Clicking on the orange arrow symbol (
) at the left hand side of a
table will identify entries with performance equivalent to the
selected submission. For each object class all entries equivalent to
the selected submission are marked by orange highlighting. - gray highlighting with double stars (**) indicate that equivalent entries are not available for this submission yet.
Average Precision (AP %)
| mean | aero plane  | bicycle | bird | boat | bottle | bus | car | cat | chair | cow | dining table | dog | horse | motor bike | person | potted plant | sheep | sofa | train | tv/ monitor | submission date | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ** YOLOv3 ** [?] | 80.8 | 91.2 | 85.8 | 83.1 | 73.5 | 68.7 | 84.2 | 85.9 | 90.2 | 66.9 | 84.4 | 65.8 | 88.9 | 87.6 | 88.6 | 89.8 | 58.5 | 85.8 | 74.0 | 88.7 | 73.9 | 05-Jan-2020 |
 | ** YOLOv3 ** [?] | 75.3 | 85.1 | 82.3 | 78.2 | 66.9 | 63.5 | 81.7 | 81.1 | 89.3 | 58.9 | 75.8 | 55.8 | 87.6 | 80.7 | 84.4 | 85.0 | 53.0 | 80.3 | 65.2 | 82.9 | 69.0 | 10-Jan-2020 |
| NUS_UDS [?] | 41.2 | 60.1 | 54.3 | 23.9 | 22.9 | 31.8 | 57.0 | 51.1 | 54.8 | 17.6 | 35.7 | 26.7 | 42.8 | 51.2 | 58.0 | 41.7 | 15.3 | 37.8 | 39.8 | 54.9 | 45.6 | 29-Oct-2014 |
| segDPM [?] | 40.4 | 61.4 | 53.4 | 25.6 | 25.2 | 35.5 | 51.7 | 50.6 | 50.8 | 19.3 | 33.8 | 26.8 | 40.4 | 48.3 | 54.4 | 47.1 | 14.8 | 38.7 | 35.0 | 52.8 | 43.1 | 24-Feb-2014 |
| Fisher with FLAIR [?] | 40.4 | 61.3 | 52.3 | 27.8 | 25.7 | 21.3 | 54.0 | 45.6 | 54.0 | 15.5 | 32.6 | 33.3 | 41.8 | 47.9 | 57.8 | 37.3 | 24.3 | 41.8 | 35.8 | 50.4 | 47.3 | 17-Jun-2014 |
| Boosted HOG-LBP and multi-context (LC, EGC, HLC) [?] | 36.8 | 53.3 | 55.3 | 19.2 | 21.0 | 30.0 | 54.5 | 46.7 | 41.2 | 20.0 | 31.5 | 20.8 | 30.3 | 48.6 | 55.3 | 46.5 | 10.2 | 34.4 | 26.6 | 50.3 | 40.3 | 29-Aug-2010 |
| MITUCLA_Hierarchy [?] | 36.0 | 54.3 | 48.5 | 15.7 | 19.2 | 29.2 | 55.6 | 43.5 | 41.7 | 16.9 | 28.5 | 26.7 | 30.9 | 48.3 | 55.0 | 41.7 | 9.7 | 35.8 | 30.8 | 47.2 | 40.8 | 30-Aug-2010 |
| HOGLBP_context_classification_rescore_v2 [?] | 34.2 | 49.1 | 52.4 | 17.8 | 12.0 | 30.6 | 53.5 | 32.8 | 37.3 | 17.7 | 30.6 | 27.7 | 29.5 | 51.9 | 56.3 | 44.2 | 9.6 | 14.8 | 27.9 | 49.5 | 38.4 | 30-Aug-2010 |
| LSVM-MDPM [?] | 33.7 | 52.4 | 54.3 | 13.0 | 15.6 | 35.1 | 54.2 | 49.1 | 31.8 | 15.5 | 26.2 | 13.5 | 21.5 | 45.4 | 51.6 | 47.5 | 9.1 | 35.1 | 19.4 | 46.6 | 38.0 | 26-Aug-2010 |
| UOCTTI_LSVM_MDPM [?] | 33.4 | 49.2 | 53.8 | 13.1 | 15.3 | 35.5 | 53.4 | 49.7 | 27.0 | 17.2 | 28.8 | 14.7 | 17.8 | 46.4 | 51.2 | 47.7 | 10.8 | 34.2 | 20.7 | 43.8 | 38.3 | 21-May-2012 |
| Detection Monkey [?] | 32.9 | 56.7 | 39.8 | 16.8 | 12.2 | 13.8 | 44.9 | 36.9 | 47.7 | 12.1 | 26.9 | 26.5 | 37.2 | 42.1 | 51.9 | 25.7 | 12.1 | 37.8 | 33.0 | 41.5 | 41.7 | 30-Aug-2010 |
| RM^2C [?] | 32.8 | 49.8 | 50.6 | 15.1 | 15.5 | 28.5 | 51.1 | 42.2 | 30.5 | 17.3 | 28.3 | 12.4 | 26.0 | 45.6 | 51.8 | 41.4 | 12.6 | 30.4 | 26.1 | 44.0 | 37.6 | 29-Oct-2013 |
| UOCTTI_LSVM_MDPM [?] | 32.2 | 48.2 | 52.2 | 14.8 | 13.8 | 28.7 | 53.2 | 44.9 | 26.0 | 18.4 | 24.4 | 13.7 | 23.1 | 45.8 | 50.5 | 43.7 | 9.8 | 31.1 | 21.5 | 44.4 | 35.7 | 11-May-2012 |
| GroupLoc [?] | 31.9 | 58.4 | 39.6 | 18.0 | 13.3 | 11.1 | 46.4 | 37.8 | 43.9 | 10.3 | 27.5 | 20.8 | 36.0 | 39.4 | 48.5 | 22.9 | 13.0 | 36.9 | 30.5 | 41.2 | 41.9 | 30-Aug-2010 |
| UOCTTI_LSVM_MDPM [?] | 29.6 | 45.6 | 49.0 | 11.0 | 11.6 | 27.2 | 50.5 | 43.1 | 23.6 | 17.2 | 23.2 | 10.7 | 20.5 | 42.5 | 44.5 | 41.3 | 8.7 | 29.0 | 18.7 | 40.0 | 34.5 | 21-May-2012 |
| Bonn_FGT_Segm [?] | 26.1 | 52.7 | 33.7 | 13.2 | 11.0 | 14.2 | 43.2 | 31.9 | 35.6 | 5.8 | 25.4 | 14.4 | 20.6 | 38.1 | 41.7 | 25.0 | 5.8 | 26.3 | 18.1 | 37.6 | 28.1 | 30-Aug-2010 |
| HOG-LBP + DHOG bag of words, SVM [?] | 23.5 | 40.4 | 34.7 | 2.7 | 8.4 | 26.0 | 43.1 | 33.8 | 17.2 | 11.2 | 14.3 | 14.5 | 14.9 | 31.8 | 37.3 | 30.0 | 6.4 | 25.2 | 11.6 | 30.0 | 35.7 | 30-Aug-2010 |
| Svr-Segm [?] | 23.4 | 50.5 | 24.5 | 17.1 | 13.3 | 10.9 | 39.5 | 32.9 | 36.5 | 5.6 | 16.0 | 6.6 | 22.3 | 24.9 | 29.0 | 29.8 | 6.7 | 28.4 | 13.3 | 32.1 | 27.2 | 30-Aug-2010 |
| HOG-LBP Linear SVM [?] | 22.1 | 37.9 | 33.7 | 2.7 | 6.5 | 25.3 | 37.5 | 33.1 | 15.5 | 10.9 | 12.3 | 12.5 | 13.7 | 29.7 | 34.5 | 33.8 | 7.2 | 22.9 | 9.9 | 28.9 | 34.1 | 29-Aug-2010 |
| HOG+LBP+LTP+PLS2ROOTS [?] | 17.5 | 32.7 | 29.7 | 0.8 | 1.1 | 19.9 | 39.4 | 27.5 | 8.6 | 4.5 | 8.1 | 6.3 | 11.0 | 22.9 | 34.1 | 24.6 | 3.1 | 24.0 | 2.0 | 23.5 | 27.0 | 31-Aug-2010 |
| RandomParts [?] | 14.2 | 23.8 | 31.7 | 1.2 | 3.4 | 11.1 | 29.7 | 19.5 | 14.2 | 0.8 | 11.1 | 7.0 | 4.7 | 16.4 | 31.5 | 16.0 | 1.1 | 15.6 | 10.2 | 14.7 | 21.0 | 25-Aug-2010 |
| SIFT-GMM-MKL2 [?] | 8.3 | 20.0 | 14.5 | 3.8 | 1.2 | 0.5 | 17.6 | 8.1 | 28.5 | 0.1 | 2.9 | 3.1 | 17.5 | 7.2 | 18.8 | 3.3 | 0.8 | 2.9 | 6.3 | 7.6 | 1.1 | 30-Aug-2010 |
| UC3M_Generative_Discriminative [?] | 6.3 | 15.8 | 5.5 | 5.6 | 2.3 | 0.3 | 10.2 | 5.4 | 12.6 | 0.5 | 5.6 | 4.5 | 7.7 | 11.3 | 12.6 | 5.3 | 1.5 | 2.0 | 5.9 | 9.1 | 3.2 | 30-Aug-2010 |
| SIFT-GMM-MKL [?] | 2.3 | 10.6 | 1.6 | 1.2 | 0.9 | 0.1 | 2.8 | 1.6 | 6.7 | 0.1 | 2.0 | 0.4 | 3.0 | 2.0 | 4.4 | 2.0 | 0.3 | 1.1 | 1.2 | 2.1 | 1.9 | 30-Aug-2010 |
| CMIC_SynthTrain [?] | - | - | 28.9 | - | - | - | 30.2 | 13.3 | - | - | - | - | - | 26.2 | 28.1 | 13.2 | - | - | - | 18.8 | 25.7 | 30-Aug-2010 |
| CMIC_VarParts [?] | - | - | 28.2 | - | - | - | 26.9 | 13.7 | - | - | - | - | - | 23.5 | 24.7 | 16.1 | - | - | - | 18.8 | 24.5 | 30-Aug-2010 |
| RandomParts_maxScore [?] | - | - | - | 2.7 | - | - | - | - | 16.2 | - | 10.6 | 8.5 | - | - | - | 17.9 | - | - | - | 15.7 | - | 30-Aug-2010 |
| DPM-SP [?] | - | 46.1 | 52.6 | 13.8 | 15.5 | 28.3 | 53.2 | 44.5 | 26.6 | 17.6 | - | 16.1 | 20.4 | 45.5 | 51.2 | 43.5 | 11.6 | 30.9 | 20.3 | 47.6 | - | 30-Aug-2010 |
Abbreviations
| Title | Method | Affiliation | Contributors | Description | Date |
|---|---|---|---|---|---|
| FG Detection, FG Tiling | Bonn_FGT_Segm | Univeristy of Bonn | João Carreira, Adrian Ion, Fuxin Li, Cristian Sminchisescu | - Detection on figure-ground segmentations; - Classification by detection; - Semantic segmentation on figure-ground tilings; | 2010-08-30 23:08:18 |
| NLPR_VSTAR_DET_4 | Boosted HOG-LBP and multi-context (LC, EGC, HLC) | National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences | tntgroup | Deformable model with Boosted HOG-LBP and multi-context information, use location context, enhanced global context, HOG and LBP inter-class context. | 2010-08-29 15:26:53 |
| Synthetic Training of Deformable Part Models | CMIC_SynthTrain | Cairo Microsoft Innovation Lab, Microsoft Research | Osama Khalil, Yasmine Badr, Motaz El-Saban | This submission applies synthetic training for Deformable Part Models. Using the segmentation mask of the objects, we synthesized new training examples, by relocating the objects to different background. The idea was applied on top of the deformable models approach [1]. [1] P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan; Object detection with discriminatively trained part based models; PAMI 2009. | 2010-08-30 21:16:16 |
| Deformable part models with variable sized parts | CMIC_VarParts | Cairo Microsoft Innovation Lab, Microsoft Research | Osama Khalil, Yasmine Badr, Motaz El-Saban | Our submission is based on the Deformable Part Models approach[1]. We allowed the model parts to have variable sizes accommodating for affine distortion that renders part sizes non-proportional. [1] P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan; Object detection with discriminatively trained part based models; PAMI 2009. | 2010-08-30 21:15:32 |
| parts based model and spatial pyramid features | DPM-SP | University of California, Irvine | Yi Yang, Charless Fowlkes | Parts model results rescored by combining with spatial pyramid based scene (global) classification results. Scene trained using svm with hist intersect kernel, rescoring trained using SVM on train+val+some_from_segmentations with parameters obtained via search. | 2010-08-30 22:31:10 |
| Detection Monkey | Detection Monkey | University of Amsterdam | Koen van de Sande Jasper Uijlings Theo Gevers Arnold Smeulders | The detection monkey is trained with SVM, dense Color SIFT, spatial pyramid and multiple iterations. | 2010-08-30 20:46:02 |
| Fisher with FLAIR | Fisher with FLAIR | University of Amsterdam | Koen van de Sande, Cees Snoek, Arnold Smeulders | Run for our CVPR2014 paper "Fisher and VLAD with FLAIR", see http://koen.me/research/flair | 2014-06-17 11:41:40 |
| Localisation with grouping window selection | GroupLoc | University of Amsterdam | Jasper Uijlings Koen van de Sande Theo Gevers Arnold Smeulders Remko Scha | Candidate windows are selected using hierarchical grouping. Classification is with SIFT, SVM-Histogram Intersection, Spatial Pyramid | 2010-08-30 21:58:30 |
| HOG+LBP+LTP+PLS2ROOTS | HOG+LBP+LTP+PLS2ROOTS | LJK,INPG | Sibt ul Hussain, Bill Triggs | This method consists of two roots based object detector. This detector is also trained using HOG+LBP+LTP as feature sets while PLS based linear SVM is used as learning algorithm. | 2010-08-31 01:14:00 |
| Linear svm classifier with bag of words method | HOG-LBP + DHOG bag of words, SVM | The University of Missouri, NEC Labs America, The University of Illinois at Urbana-Champaign | Xiaoyu Wang, Xi Zhou, Tony X. Han, Shuai Tang, Guang Chen, Kai Yu, Thomas S. Huang | Liblinear SVM with HOG-LBP feature and DHOG bag of words approach | 2010-08-30 18:07:43 |
| svm classifier with HOG LBP features | HOG-LBP Linear SVM | The University of Missouri, NEC Labs America, The University of Illinois at Urbana-Champaign | Xiaoyu Wang, Xi Zhou, Tony X. Han, Shuai Tang, Guang Chen, Kai Yu, Thomas S. Huang | Liblinear SVM with HOG-LBP features. All classes use the same default training parameters. | 2010-08-29 07:14:40 |
| results refined by context and classification | HOGLBP_context_classification_rescore_v2 | National University of Singapore | Zheng Song, Qiang Chen, Shuicheng Yan | Use HOG+LBP trained part-based detector. The detection results are further reranked via the context information of other detect windows and image classificaton scores. | 2010-08-30 04:20:46 |
| LSVM Mixtures of deformable part models | LSVM-MDPM | University of Chicago and TTI-C | Pedro Felzenszwalb (UChicago), Ross Girshick (UChicago), David McAllester (TTI-C) | Our submission is based on an extension of [1,2]. Each model has 6 components with 8 parts. We associate a binary mask with each component to generate segmentations. The object detection models were trained from bounding boxes. The segmentation masks were trained from segmentations. [1] P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan. Object Detection with Discriminatively Trained Part Based Models. PAMI 32(9), Sept 2010 [2] http://people.cs.uchicago.edu/~pff/latent-release4/ | 2010-08-26 04:07:34 |
| Latent hierarchical structural learning | MITUCLA_Hierarchy | MIT and UCLA | Long Zhu, Yuanhao Chen, William Freeman, Alan Yuille, Antonio Torralba | Latent hierarchical structural learning with dense HOG and HOW(SIFT) features. | 2010-08-30 20:58:06 |
| Unified Object Detection and Semantic Segmentation | NUS_UDS | NUS | Jian Dong, Qiang Chen, Shuicheng Yan, Alan Yuille | Motivated by the complementary effect observed from the typical failure cases of object detection and semantic segmentation, we propose a uni?ed framework for joint object detection and semantic segmentation [1]. By enforcing the consistency between final detection and segmentation results, our unified framework can effectively leverage the advantages of leading techniques for these two tasks. Furthermore, both local and global context information are integrated into the framework to better distinguish the ambiguous samples. By jointly optimizing the model parameters for all the components, the relative importance of different component is automatically learned for each category to guarantee the overall performance. [1] Jian Dong, Qiang Chen, Shuicheng Yan, Alan Yuille: Towards Unified Object Detection and Semantic Segmentation. ECCV 2014 | 2014-10-29 15:57:03 |
| Randomized Max-Margin Compositions | RM^2C | University of Heidelberg, IWR/HCI | Angela Eigenstetter, Masato Takami, Björn Ommer | We are grouping a large number of randomly sampled parts into fewer, overlapping compositions that are trained using a maximum-margin approach. For more details see our CVPR 2014 Paper "Randomized Max-Margin Compositions for Visual Recognition". Parts are available for download on our project page http://hci.iwr.uni-heidelberg.de/COMPVIS/research/RM2C/ . | 2013-10-29 08:25:28 |
| Unsupervised Parts-based Attributes | RandomParts | Carnegie Mellon University | Santosh Divvala (CMU) Larry Zitnick (MSR) Ashish Kapoor (MSR) Simon Baker (MSR) | http://www.cs.cmu.edu/~santosh/finalReport.pdf (unpublished work) | 2010-08-25 21:50:35 |
| Unsupervised Parts-based Attributes (max score) | RandomParts_maxScore | Carnegie Mellon University | Santosh Divvala (CMU) Larry Zitnick (MSR) Ashish Kapoor (MSR) Simon Baker (MSR) | Updated version of earlier submission (http://www.cs.cmu.edu/~santosh/finalReport.pdf). Main update: inclusion of max score feature and one round of iterative training | 2010-08-30 23:10:27 |
| Multiple kernel learning with SIFT GMMs | SIFT-GMM-MKL | Tokyo Institute of Technology | Nakamasa Inoue, Yusuke Kamishima, Koichi Shinoda | We use multiple kernel learning and GMM supervector kernels with SIFT features. | 2010-08-30 04:39:41 |
| Multiple kernel learning with SIFT GMMs | SIFT-GMM-MKL2 | Tokyo Institute of Technology | Nakamasa Inoue, Yusuke Kamishima, Koichi Shinoda | Same as the SIFT-GMM-MKL run but the GrabCut is applied for detection. | 2010-08-30 07:35:56 |
| Svr-Segm | Svr-Segm | University of Bonn | Joao Carreira, Fuxin Li, Adrian Ion, Cristian Sminchisescu | Support vector regression to multiple descriptors extracted on segmentations. Descriptors include SIFT, color SIFT and HOG on foreground and background. Post-processing to eliminate spurious detections and segmentations. The winning method of 2009 challenge. | 2010-08-30 22:52:55 |
| Combination of Generative Discriminative Methods | UC3M_Generative_Discriminative | Universidad Carlos III de Madrid | Iván González-Díaz, Fernando Díaz de María | Combination of Supervised Topic Models with SVM-based discriminative methods for concurrent image recognition and segmentation | 2010-08-30 13:27:41 |
| LSVM mixtures of deformable part models | UOCTTI_LSVM_MDPM | University of Chicago | Ross Girshick | voc-release5 with extra octave and star-cascade | 2012-05-21 05:14:06 |
| LSVM mixtures of deformable part models | UOCTTI_LSVM_MDPM | University of Chicago | Ross Girshick | voc-release5 | 2012-05-11 05:14:49 |
| LSVM Mixtures of deformable part models | UOCTTI_LSVM_MDPM | University of Chicago | Ross Girshick | voc-release5 without context | 2012-05-21 17:39:28 |
| yolov3_pytorch | YOLOv3 | szu | yuanliang xie yishuang zhu | just for a test? | 2020-01-05 07:42:44 |
| yolov3_pytorch | YOLOv3 | szu | yuanliang xie yunbiao li | image_size:544x544 have not pull on the github maybe in feature github url:https://github.com/yuanliangxie | 2020-01-10 09:45:21 |
| DPM that uses region segmentation features | segDPM | UofT, TTI-C, UCLA | Sanja Fidler, Roozbeh Mottaghi, Allan Yuille, Raquel Urtasun | DPM-style model that exploits bottom-up segmentation (CPMC regions). In addition, we use context re-scoring based on object presence classifiers provided by NUS. Project page: http://www.cs.toronto.edu/~fidler/projects/segDPM.html | 2014-02-24 19:53:33 |