Model Zoo Statistics

• Number of papers: 59
• Number of checkpoints: 366
  • Albu Example (1 ckpts)
  • Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection (2 ckpts)
  • CARAFE: Content-Aware ReAssembly of FEatures (2 ckpts)
  • Cascade R-CNN: High Quality Object Detection and Instance Segmentation (20 ckpts)
  • Cascade RPN (3 ckpts)
  • CentripetalNet (1 ckpts)
  • Cityscapes Dataset (2 ckpts)
  • CornerNet (3 ckpts)
  • Deformable Convolutional Networks (15 ckpts)
  • DeepFashion (1 ckpts)
  • DetectoRS (6 ckpts)
  • DETR (1 ckpts)
  • Rethinking Classification and Localization for Object Detection (1 ckpts)
  • Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training (1 ckpts)
  • An Empirical Study of Spatial Attention Mechanisms in Deep Networks (4 ckpts)
  • Fast R-CNN (0 ckpts)
  • Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (20 ckpts)
  • FCOS: Fully Convolutional One-Stage Object Detection (9 ckpts)
  • FoveaBox: Beyond Anchor-based Object Detector (8 ckpts)
  • Mixed Precision Training (3 ckpts)
  • FreeAnchor: Learning to Match Anchors for Visual Object Detection (3 ckpts)
  • Feature Selective Anchor-Free Module for Single-Shot Object Detection (4 ckpts)
  • GCNet for Object Detection (19 ckpts)
  • Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection (6 ckpts)
  • Gradient Harmonized Single-stage Detector (4 ckpts)
  • Weight Standardization (12 ckpts)
  • Group Normalization (6 ckpts)
  • Grid R-CNN (4 ckpts)
  • GRoIE (9 ckpts)
  • Region Proposal by Guided Anchoring (12 ckpts)
  • High-resolution networks (HRNets) for object detection (28 ckpts)
  • Hybrid Task Cascade for Instance Segmentation (6 ckpts)
  • InstaBoost for MMDetection (4 ckpts)
  • Legacy Configs in MMDetection V1.x (4 ckpts)
  • Libra R-CNN: Towards Balanced Learning for Object Detection (4 ckpts)
  • LVIS dataset (8 ckpts)
  • Mask R-CNN (12 ckpts)
  • Mask Scoring R-CNN (7 ckpts)
  • NAS-FCOS: Fast Neural Architecture Search for Object Detection (2 ckpts)
  • NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection (2 ckpts)
  • Probabilistic Anchor Assignment with IoU Prediction for Object Detection (5 ckpts)
  • Path Aggregation Network for Instance Segmentation (1 ckpts)
  • PASCAL VOC Dataset (2 ckpts)
  • Prime Sample Attention in Object Detection (7 ckpts)
  • PointRend (2 ckpts)
  • Designing Network Design Spaces (18 ckpts)
  • RepPoints: Point Set Representation for Object Detection (8 ckpts)
  • Res2Net for object detection and instance segmentation (5 ckpts)
  • ResNeSt: Split-Attention Networks (8 ckpts)
  • Focal Loss for Dense Object Detection (10 ckpts)
  • Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (10 ckpts)
  • Side-Aware Boundary Localization for More Precise Object Detection (10 ckpts)
  • Rethinking ImageNet Pre-training (2 ckpts)
  • SSD: Single Shot MultiBox Detector (2 ckpts)
  • Scale-Aware Trident Networks for Object Detection (3 ckpts)
  • VarifocalNet: An IoU-aware Dense Object Detector (8 ckpts)
  • WIDER Face Dataset (0 ckpts)
  • You Only Look At CoefficienTs (3 ckpts)
  • YOLOv3 (3 ckpts)