1506.02640v5

Sep 2, 2026

Object detection performance results are presented across multiple methods and datasets, using metrics such as mAP and AP. Comparisons include YOLO, Fast YOLO, Fast R-CNN variants, Faster R-CNN, MR CNN variants, HyperNet variants, DEEP ENS COCO, and other approaches. The tables report numeric scores for class-wise categories on VOC-style evaluations and summarize outcomes on VOC 2007 AP alongside additional benchmarks like People-Art AP, enabling method ranking and trade-off analysis.

LanguageEnglish

CategoryTechnology

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Results comparison

VOC 2012 mAP

VOC 2007 AP and additional benchmarks

| 100Hz DPM [31] | 2007 | 16.0 | 100 | | --- | --- | --- | --- | | 30Hz DPM [31] | 2007 | 26.1 | 30 | | Fast YOLO | 2007+2012 | 52.7 | 155 | | YOLO | 2007+2012 | 63.4 | 45 | | Less Than Real-Time | | | | | VOC 2012 test | mAP | aero bike bird boat bottle bus car cat chair cow table dog horse mbike personplant sheep sofa train tv | | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | MR CNN MORE DATA [11] | 73.9 | 85.5 | 82.9 | 76.6 | 57.8 | 62.7 | 79.4 | 77.2 | 86.6 | 55.0 | 79.1 | 62.2 | 87.0 | 83.4 | 84.7 | 78.9 | 45.3 | 73.4 | 65.8 | 80.3 | 74.0 | | HyperNet VGG | 71.4 | 84.2 | 78.5 | 73.6 | 55.6 | 53.7 | 78.7 | 79.8 | 87.7 | 49.6 | 74.9 | 52.1 | 86.0 | 81.7 | 83.3 | 81.8 | 48.6 | 73.5 | 59.4 | 79.9 | 65.7 | | HyperNet SP | 71.3 | 84.1 | 78.3 | 73.3 | 55.5 | 53.6 | 78.6 | 79.6 | 87.5 | 49.5 | 74.9 | 52.1 | 85.6 | 81.6 | 83.2 | 81.6 | 48.4 | 73.2 | 59.3 | 79.7 | 65.6 | | Fast R-CNN + YOLO | 70.7 | 83.4 | 78.5 | 73.5 | 55.8 | 43.4 | 79.1 | 73.1 | 89.4 | 49.4 | 75.5 | 57.0 | 87.5 | 80.9 | 81.0 | 74.7 | 41.8 | 71.5 | 68.5 | 82.1 | 67.2 | | MR CNN S CNN [11] | 70.7 | 85.0 | 79.6 | 71.5 | 55.3 | 57.7 | 76.0 | 73.9 | 84.6 | 50.5 | 74.3 | 61.7 | 85.5 | 79.9 | 81.7 | 76.4 | 41.0 | 69.0 | 61.2 | 77.7 | 72.1 | | Faster R-CNN [28] | 70.4 | 84.9 | 79.8 | 74.3 | 53.9 | 49.8 | 77.5 | 75.9 | 88.5 | 45.6 | 77.1 | 55.3 | 86.9 | 81.7 | 80.9 | 79.6 | 40.1 | 72.6 | 60.9 | 81.2 | 61.5 | | DEEP ENS COCO | 70.1 | 84.0 | 79.4 | 71.6 | 51.9 | 51.1 | 74.1 | 72.1 | 88.6 | 48.3 | 73.4 | 57.8 | 86.1 | 80.0 | 80.7 | 70.4 | 46.6 | 69.6 | 68.8 | 75.9 | 71.4 | | NoC [29] | 68.8 | 82.8 | 79.0 | 71.6 | 52.3 | 53.7 | 74.1 | 69.0 | 84.9 | 46.9 | 74.3 | 53.1 | 85.0 | 81.3 | 79.5 | 72.2 | 38.9 | 72.4 | 59.5 | 76.7 | 68.1 | | Fast R-CNN [14] | 68.4 | 82.3 | 78.4 | 70.8 | 52.3 | 38.7 | 77.8 | 71.6 | 89.3 | 44.2 | 73.0 | 55.0 | 87.5 | 80.5 | 80.8 | 72.0 | 35.1 | 68.3 | 65.7 | 80.4 | 64.2 | | UMICH FGS STRUCT | 66.4 | 82.9 | 76.1 | 64.1 | 44.6 | 49.4 | 70.3 | 71.2 | 84.6 | 42.7 | 68.6 | 55.8 | 82.7 | 77.1 | 79.9 | 68.7 | 41.4 | 69.0 | 60.0 | 72.0 | 66.2 | | NUS NIN C2000 [7] | 63.8 | 80.2 | 73.8 | 61.9 | 43.7 | 43.0 | 70.3 | 67.6 | 80.7 | 41.9 | 69.7 | 51.7 | 78.2 | 75.2 | 76.9 | 65.1 | 38.6 | 68.3 | 58.0 | 68.7 | 63.3 | | BabyLearning [7] | 63.2 | 78.0 | 74.2 | 61.3 | 45.7 | 42.7 | 68.2 | 66.8 | 80.2 | 40.6 | 70.0 | 49.8 | 79.0 | 74.5 | 77.9 | 64.0 | 35.3 | 67.9 | 55.7 | 68.7 | 62.6 | | NUS NIN | 62.4 | 77.9 | 73.1 | 62.6 | 39.5 | 43.3 | 69.1 | 66.4 | 78.9 | 39.1 | 68.1 | 50.0 | 77.2 | 71.3 | 76.1 | 64.7 | 38.4 | 66.9 | 56.2 | 66.9 | 62.7 | | R-CNN VGG BB [13] | 62.4 | 79.6 | 72.7 | 61.9 | 41.2 | 41.9 | 65.9 | 66.4 | 84.6 | 38.5 | 67.2 | 46.7 | 82.0 | 74.8 | 76.0 | 65.2 | 35.6 | 65.4 | 54.2 | 67.4 | 60.3 | | R-CNN VGG [13] | 59.2 | 76.8 | 70.9 | 56.6 | 37.5 | 36.9 | 62.9 | 63.6 | 81.1 | 35.7 | 64.3 | 43.9 | 80.4 | 71.6 | 74.0 | 60.0 | 30.8 | 63.4 | 52.0 | 63.5 | 58.7 | | YOLO | 57.9 | 77.0 | 67.2 | 57.7 | 38.3 | 22.7 | 68.3 | 55.9 | 81.4 | 36.2 | 60.8 | 48.5 | 77.2 | 72.3 | 71.3 | 63.5 | 28.9 | 52.2 | 54.8 | 73.9 | 50.8 | | Feature Edit [33] | 56.3 | 74.6 | 69.1 | 54.4 | 39.1 | 33.1 | 65.2 | 62.7 | 69.7 | 30.8 | 56.0 | 44.6 | 70.0 | 64.4 | 71.1 | 60.2 | 33.3 | 61.3 | 46.4 | 61.7 | 57.8 | | R-CNN BB [13] | 53.3 | 71.8 | 65.8 | 52.0 | 34.1 | 32.6 | 59.6 | 60.0 | 69.8 | 27.6 | 52.0 | 41.7 | 69.6 | 61.3 | 68.3 | 57.8 | 29.6 | 57.8 | 40.9 | 59.3 | 54.1 | | SDS [16] | 50.7 | 69.7 | 58.4 | 48.5 | 28.3 | 28.8 | 61.3 | 57.5 | 70.8 | 24.1 | 50.7 | 35.9 | 64.9 | 59.1 | 65.8 | 57.1 | 26.0 | 58.8 | 38.6 | 58.9 | 50.7 | | R-CNN [13] | 49.6 | 68.1 | 63.8 | 46.1 | 29.4 | 27.9 | 56.6 | 57.0 | 65.9 | 26.5 | 48.7 | 39.5 | 66.2 | 57.3 | 65.4 | 53.2 | 26.2 | 54.5 | 38.1 | 50.6 | 51.6 | | | VOC 2007 AP | Picasso | | People-Art AP | | --- | --- | --- | --- | --- | | | | AP | Best F1 | | | YOLO | 59.2 | 53.3 | 0.590 | 45 | | R-CNN | 54.2 |

FAQs

Which evaluation metrics are used in the tables?

The document reports mAP for VOC 2012 test and AP (including VOC 2007 AP) for additional comparisons, sometimes paired with best F1 and People-Art AP.

What detection methods are compared?

Methods include YOLO and Fast YOLO, R-CNN and Fast R-CNN variants, Faster R-CNN, MR CNN/S CNN, HyperNet variants, DEEP ENS COCO, and other named approaches shown in the tables.

How are the results organized by dataset or benchmark?

Results are grouped under VOC 2012 test class-wise mAP tables and a separate section listing VOC 2007 AP with additional benchmark columns such as Picasso and People-Art AP.

Sep 2, 2026

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