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SOTA
物体检测
Object Detection On Coco
Object Detection On Coco
评估指标
AP50
AP75
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
AP50
AP75
Paper Title
Repository
DyHead (ResNet-101)
64.5
50.7
Dynamic Head: Unifying Object Detection Heads with Attentions
YOLOv7-D6 (44 fps)
-
-
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
EfficientDet-D7 (1536)
71.6
56.9
EfficientDet: Scalable and Efficient Object Detection
GFLV2 (ResNeXt-101, 32x4d, DCN)
67.6
53.5
Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection
Faster R-CNN (ImageNet+300M)
58
40.1
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
GLIP (Swin-L, multi-scale)
79.5
67.7
Grounded Language-Image Pre-training
Group DETR v2
81.8
71.1
Group DETR v2: Strong Object Detector with Encoder-Decoder Pretraining
-
Mask R-CNN (ResNeXt-101-FPN)
62.3
43.4
Mask R-CNN
ISTR (ResNet50-FPN-3x, single-scale)
-
-
ISTR: End-to-End Instance Segmentation with Transformers
CPNDet (Hourglass-104, multi-scale)
67.3
53.7
Corner Proposal Network for Anchor-free, Two-stage Object Detection
M2Det (VGG-16, multi-scale)
64.6
49.3
M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network
YOLOv7-E6 (56 fps)
-
-
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
A2MIM (ViT-B)
-
-
Architecture-Agnostic Masked Image Modeling -- From ViT back to CNN
D-RFCN + SNIP (ResNet-101, multi-scale)
65.5
48.4
An Analysis of Scale Invariance in Object Detection - SNIP
-
LeYOLO-nano@480
-
-
LeYOLO, New Embedded Architecture for Object Detection
Centermask + ResNet101
61.6
46.9
CenterMask : Real-Time Anchor-Free Instance Segmentation
AC-FPN Cascade R-CNN (X-152-32x8d-FPN-IN5k, multi scale, only CEM)
70.4
57
Attention-guided Context Feature Pyramid Network for Object Detection
MnasFPN (MNASNet-B1)
-
-
MnasFPN: Learning Latency-aware Pyramid Architecture for Object Detection on Mobile Devices
Gaussian-FCOS
-
-
Localization Uncertainty Estimation for Anchor-Free Object Detection
-
Faster R-CNN
-
-
Speed/accuracy trade-offs for modern convolutional object detectors
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