mlsanityData to 7 Oct 2026

MMDetection: Open MMLab Detection Toolbox and Benchmark

arXiv 1906.07155 ↗First posted 17 Jun 2019cs.CV cs.LG eess.IVcited by 1,379

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Cited by 25 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We present MMDetection, an object detection toolbox that contains a rich set of object detection and instance segmentation methods as well as related components and modules. The toolbox started from a codebase of MMDet team who won the detection track of COCO Challenge 2018. It gradually evolves into a unified platform that covers many popular detection methods and contemporary modules. It not only includes training and inference codes, but also provides weights for more than 200 network models. We believe this toolbox is by far the most complete detection toolbox. In this paper, we introduce the various features of this toolbox. In addition, we also conduct a benchmarking study on different methods, components, and their hyper-parameters. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new detectors. Code and models are available at https://github.com/open-mmlab/mmdetection. The project is under active development and we will keep this document updated.

Key facts. Cited by 1,379 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jun 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 246; 2022: 303; 2023: 283; 2024: 287; 2025: 173; 2026: 87.

1,379 papers.

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  1. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  2. 2022A ConvNet for the 2020scited by 3,545
  3. 2021Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutionscited by 1,960
  4. 2022DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detectioncited by 1,306
  5. 2021Swin Transformer V2: Scaling Up Capacity and Resolutioncited by 1,215
  6. 2024VMamba: Visual State Space Modelcited by 955
  7. 2023GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignmentcited by 946
  8. 2021PVT v2: Improved Baselines with Pyramid Vision Transformercited by 749
  9. 2023ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoderscited by 703
  10. 2024Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Surveycited by 641
  11. 2021Transformer in Transformercited by 632
  12. 2021Twins: Revisiting the Design of Spatial Attention in Vision Transformerscited by 565
38 more
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  3. 2021CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windowscited by 526
  4. 2021MetaFormer Is Actually What You Need for Visioncited by 523
  5. 2024YOLO-World: Real-Time Open-Vocabulary Object Detectioncited by 498
  6. 2022DiffusionDet: Diffusion Model for Object Detectioncited by 354
  7. 2021XCiT: Cross-Covariance Image Transformerscited by 335
  8. 2021End-to-End Semi-Supervised Object Detection with Soft Teachercited by 315
  9. 2022Context Autoencoder for Self-Supervised Representation Learningcited by 297
  10. 2022Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuningcited by 292
  11. 2022DETRs with Collaborative Hybrid Assignments Trainingcited by 273
  12. 2023GPT4Tools: Teaching Large Language Model to Use Tools via Self-instructioncited by 245
  13. 2022Neighborhood Attention Transformercited by 235
  14. 2024LocalMamba: Visual State Space Model with Windowed Selective Scancited by 235
  15. 2022What the DAAM: Interpreting Stable Diffusion Using Cross Attentioncited by 233
  16. 2021Oriented R-CNN for Object Detectioncited by 230
  17. 2021Co-Scale Conv-Attentional Image Transformerscited by 224
  18. 2021TOOD: Task-aligned One-stage Object Detectioncited by 218
  19. 2023Sequential Modeling Enables Scalable Learning for Large Vision Modelscited by 212
  20. 2021K-Net: Towards Unified Image Segmentationcited by 211
  21. 2022Visual Attention Networkcited by 210
  22. 2022UniFormer: Unifying Convolution and Self-attention for Visual Recognitioncited by 206
  23. 2021PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformerscited by 204
  24. 2021Object Detection in Aerial Images: A Large-Scale Benchmark and Challengescited by 202
  25. 2021ReDet: A Rotation-equivariant Detector for Aerial Object Detectioncited by 202
  26. 2021Embracing Single Stride 3D Object Detector with Sparse Transformercited by 193
  27. 2022MaskCLIP: Masked Self-Distillation Advances Contrastive Language-Image Pretrainingcited by 187
  28. 2021Learning N:M Fine-grained Structured Sparse Neural Networks From Scratchcited by 177
  29. 2022DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detectioncited by 176
  30. 2022Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Modelscited by 172
  31. 2023EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attentioncited by 172
  32. 2021Conformer: Local Features Coupling Global Representations for Visual Recognitioncited by 164
  33. 2022Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wildcited by 159
  34. 2021Instances as Queriescited by 157
  35. 2021ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detectioncited by 157
  36. 2022Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNscited by 155
  37. 2023BiFormer: Vision Transformer with Bi-Level Routing Attentioncited by 150
  38. 2023DDP: Diffusion Model for Dense Visual Predictioncited by 148

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