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Models/Faster R-CNN

Faster R-CNN

Reported on 68 benchmarks across 6 tasks · 5 papers · 15 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology52 results

3DonUA-DETRAC
mAP· 2015-06-04
58.45
best: 90.39 (VSTAM)
SOTA
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks arXiv:1506.01497
3DonPASCAL VOC 2007 (15+5)
FPS· 2015-06-04
7
SOTA
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks arXiv:1506.01497
3DonPASCAL VOC 2007 (15+5)
MAP· 2015-06-04
73.2
SOTA
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks arXiv:1506.01497
2D ClassificationonUA-DETRAC
mAP· 2015-06-04
58.45
best: 90.39 (VSTAM)
SOTA
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks arXiv:1506.01497
2D ClassificationonPASCAL VOC 2007 (15+5)
FPS· 2015-06-04
7
SOTA
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks arXiv:1506.01497
2D ClassificationonPASCAL VOC 2007 (15+5)
MAP· 2015-06-04
73.2
SOTA
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks arXiv:1506.01497
2D Object DetectiononUA-DETRAC
mAP· 2015-06-04
58.45
best: 90.39 (VSTAM)
SOTA
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks arXiv:1506.01497
2D Object DetectiononPASCAL VOC 2007 (15+5)
FPS· 2015-06-04
7
SOTA
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks arXiv:1506.01497
2D Object DetectiononPASCAL VOC 2007 (15+5)
MAP· 2015-06-04
73.2
SOTA
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks arXiv:1506.01497
16konUA-DETRAC
mAP· 2015-06-04
58.45
best: 90.39 (VSTAM)
SOTA
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks arXiv:1506.01497
16konPASCAL VOC 2007 (15+5)
FPS· 2015-06-04
7
SOTA
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks arXiv:1506.01497
16konPASCAL VOC 2007 (15+5)
MAP· 2015-06-04
73.2
SOTA
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks arXiv:1506.01497
3DonWaterScenes
mAP@50-95· 2023-07-13
47.8
best: 59.2 (YOLOv8-M)
WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmarks for Autonomous Driving on Water Surfaces arXiv:2307.06505
2D ClassificationonWaterScenes
mAP@50-95· 2023-07-13
47.8
best: 59.2 (YOLOv8-M)
WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmarks for Autonomous Driving on Water Surfaces arXiv:2307.06505
2D Object DetectiononWaterScenes
mAP@50-95· 2023-07-13
47.8
best: 59.2 (YOLOv8-M)
WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmarks for Autonomous Driving on Water Surfaces arXiv:2307.06505
16konWaterScenes
mAP@50-95· 2023-07-13
47.8
best: 59.2 (YOLOv8-M)
WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmarks for Autonomous Driving on Water Surfaces arXiv:2307.06505
3DonManga109-s 15test
COCO-style AP· uses extra data· 2021-03-25
65.8
best: 70.2 (YOLOX-L)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
3DonUSB (Standard USB 1.0 protocol)
mCAP· 2021-03-25
45.9
best: 52.1 (UniverseNet-20.08)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
3DonWaymo 2D detection all_ns f0val
COCO-style AP· uses extra data· 2021-03-25
34.5
best: 41.6 (YOLOX-L)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
2D ClassificationonManga109-s 15test
COCO-style AP· uses extra data· 2021-03-25
65.8
best: 70.2 (YOLOX-L)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
2D ClassificationonUSB (Standard USB 1.0 protocol)
mCAP· 2021-03-25
45.9
best: 52.1 (UniverseNet-20.08)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
2D ClassificationonWaymo 2D detection all_ns f0val
COCO-style AP· uses extra data· 2021-03-25
34.5
best: 41.6 (YOLOX-L)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
2D Object DetectiononManga109-s 15test
COCO-style AP· uses extra data· 2021-03-25
65.8
best: 70.2 (YOLOX-L)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
2D Object DetectiononUSB (Standard USB 1.0 protocol)
mCAP· 2021-03-25
45.9
best: 52.1 (UniverseNet-20.08)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
2D Object DetectiononWaymo 2D detection all_ns f0val
COCO-style AP· uses extra data· 2021-03-25
34.5
best: 41.6 (YOLOX-L)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
16konManga109-s 15test
COCO-style AP· uses extra data· 2021-03-25
65.8
best: 70.2 (YOLOX-L)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
16konUSB (Standard USB 1.0 protocol)
mCAP· 2021-03-25
45.9
best: 52.1 (UniverseNet-20.08)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
16konWaymo 2D detection all_ns f0val
COCO-style AP· uses extra data· 2021-03-25
34.5
best: 41.6 (YOLOX-L)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
3DonPASCAL VOC 2007
mPC [AP50]· 2019-07-17
48.6
best: 56.2 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
3DonPASCAL VOC 2007
rPC [%]· 2019-07-17
60.4
best: 69.9 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
3DonCityscapes test
mPC [AP]· 2019-07-17
12.2
best: 17.2 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
3DonCityscapes test
rPC [%]· 2019-07-17
33.4
best: 47.4 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
3DonCOCO (Common Objects in Context)
mPC [AP]· 2019-07-17
18.2
best: 20.4 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
3DonCOCO (Common Objects in Context)
rPC [%]· 2019-07-17
50.2
best: 58.9 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
2D ClassificationonPASCAL VOC 2007
mPC [AP50]· 2019-07-17
48.6
best: 56.2 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
2D ClassificationonPASCAL VOC 2007
rPC [%]· 2019-07-17
60.4
best: 69.9 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
2D ClassificationonCityscapes test
mPC [AP]· 2019-07-17
12.2
best: 17.2 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
2D ClassificationonCityscapes test
rPC [%]· 2019-07-17
33.4
best: 47.4 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
2D ClassificationonCOCO (Common Objects in Context)
mPC [AP]· 2019-07-17
18.2
best: 20.4 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
2D ClassificationonCOCO (Common Objects in Context)
rPC [%]· 2019-07-17
50.2
best: 58.9 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
2D Object DetectiononPASCAL VOC 2007
mPC [AP50]· 2019-07-17
48.6
best: 56.2 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
2D Object DetectiononPASCAL VOC 2007
rPC [%]· 2019-07-17
60.4
best: 69.9 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
2D Object DetectiononCityscapes test
mPC [AP]· 2019-07-17
12.2
best: 17.2 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
2D Object DetectiononCityscapes test
rPC [%]· 2019-07-17
33.4
best: 47.4 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
2D Object DetectiononCOCO (Common Objects in Context)
mPC [AP]· 2019-07-17
18.2
best: 20.4 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
2D Object DetectiononCOCO (Common Objects in Context)
rPC [%]· 2019-07-17
50.2
best: 58.9 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
16konPASCAL VOC 2007
mPC [AP50]· 2019-07-17
48.6
best: 56.2 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
16konPASCAL VOC 2007
rPC [%]· 2019-07-17
60.4
best: 69.9 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
16konCityscapes test
mPC [AP]· 2019-07-17
12.2
best: 17.2 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
16konCityscapes test
rPC [%]· 2019-07-17
33.4
best: 47.4 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
16konCOCO (Common Objects in Context)
mPC [AP]· 2019-07-17
18.2
best: 20.4 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
16konCOCO (Common Objects in Context)
rPC [%]· 2019-07-17
50.2
best: 58.9 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484

Computer Vision13 results

Object DetectiononUA-DETRAC
mAP· 2015-06-04
58.45
best: 90.39 (VSTAM)
SOTA
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks arXiv:1506.01497
Object DetectiononPASCAL VOC 2007 (15+5)
FPS· 2015-06-04
7
SOTA
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks arXiv:1506.01497
Object DetectiononPASCAL VOC 2007 (15+5)
MAP· 2015-06-04
73.2
SOTA
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks arXiv:1506.01497
Object DetectiononWaterScenes
mAP@50-95· 2023-07-13
47.8
best: 59.2 (YOLOv8-M)
WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmarks for Autonomous Driving on Water Surfaces arXiv:2307.06505
Object DetectiononManga109-s 15test
COCO-style AP· uses extra data· 2021-03-25
65.8
best: 70.2 (YOLOX-L)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
Object DetectiononUSB (Standard USB 1.0 protocol)
mCAP· 2021-03-25
45.9
best: 52.1 (UniverseNet-20.08)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
Object DetectiononWaymo 2D detection all_ns f0val
COCO-style AP· uses extra data· 2021-03-25
34.5
best: 41.6 (YOLOX-L)
USB: Universal-Scale Object Detection Benchmark arXiv:2103.14027
Object DetectiononPASCAL VOC 2007
mPC [AP50]· 2019-07-17
48.6
best: 56.2 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
Object DetectiononPASCAL VOC 2007
rPC [%]· 2019-07-17
60.4
best: 69.9 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
Object DetectiononCityscapes test
mPC [AP]· 2019-07-17
12.2
best: 17.2 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
Object DetectiononCityscapes test
rPC [%]· 2019-07-17
33.4
best: 47.4 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
Object DetectiononCOCO (Common Objects in Context)
mPC [AP]· 2019-07-17
18.2
best: 20.4 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484
Object DetectiononCOCO (Common Objects in Context)
rPC [%]· 2019-07-17
50.2
best: 58.9 (Faster R-CNN with Stylized Training Data)
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming arXiv:1907.07484

Robots3 results

Autonomous VehiclesonDFG traffic-sign dataset
mAP @0.5:0.95· 2019-04-01
80.4
best: 84.4 (Mask R-CNN with adaptations for traffic sings and augmentations (ResNet50))
Deep Learning for Large-Scale Traffic-Sign Detection and Recognition arXiv:1904.00649
Autonomous VehiclesonDFG traffic-sign dataset
mAP@0.50· 2019-04-01
92.4
best: 95.5 (Mask R-CNN with adaptations for traffic sings and augmentations (ResNet50))
Deep Learning for Large-Scale Traffic-Sign Detection and Recognition arXiv:1904.00649
Autonomous VehiclesonSwedish traffic-sign dataset (STSD)
mAP@0.50· 2019-04-01
94.3
best: 95.2 (Mask R-CNN with adaptations for traffic sings (ResNet50))
Deep Learning for Large-Scale Traffic-Sign Detection and Recognition arXiv:1904.00649