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SOTA
Object Detection
Object Detection On Pascal Voc 2007
Object Detection On Pascal Voc 2007
Metrics
MAP
Results
Performance results of various models on this benchmark
Columns
Model Name
MAP
Paper Title
Cascade Eff-B7 NAS-FPN (Copy Paste pre-training, single-scale)
89.3%
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
YOLO-Former
86.01%
YOLO-Former: YOLO Shakes Hand With ViT
DETReg (MDef-DETR)
84.16%
Class-agnostic Object Detection with Multi-modal Transformer
HSD (VGG16, 512x512, single-scale test)
83.0%
Hierarchical Shot Detector
CoupleNet
82.7%
CoupleNet: Coupling Global Structure with Local Parts for Object Detection
EEEA-Net-C2 (YOLOv4)
81.8%
EEEA-Net: An Early Exit Evolutionary Neural Architecture Search
HSD (VGG16, 320x320, single-scale test)
81.7%
Hierarchical Shot Detector
SSD512 (07+12+COCO)
81.6%
SSD: Single Shot MultiBox Detector
BlitzNet512 + seg (s8)
81.5%
BlitzNet: A Real-Time Deep Network for Scene Understanding
Localize
81.5%
Localize to Classify and Classify to Localize: Mutual Guidance in Object Detection
CenterNet(DLA34, Flip, 512x512)
80.7%
Objects as Points
PS-KD (ResNet-152, CutMix)
79.7%
Self-Knowledge Distillation with Progressive Refinement of Targets
DPNet
79.2%
DPNet: Dual-Path Network for Real-time Object Detection with Lightweight Attention
OHEM
78.9%
Training Region-based Object Detectors with Online Hard Example Mining
YOLO v2
78.6%
YOLO9000: Better, Faster, Stronger
ThunderNet SNet535 Backbone
78.6%
ThunderNet: Towards Real-time Generic Object Detection
DeNet-101 (skip)
77.1%
DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling
I+ORE
76.2%
Random Erasing Data Augmentation
Perona Malik (Perona and Malik, 1990)
74.37%
Learning Visual Representations for Transfer Learning by Suppressing Texture
FRCN
74.2%
A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
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Object Detection On Pascal Voc 2007 | SOTA | HyperAI