Papers with Code 아카이브
Object Detection
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/open-mmlab/mmdetection3d
공식 코드Exploring Data Augmentation for Multi-Modality 3D Object Detection
https://github.com/open-mmlab/mmrazor
공식 코드PKD: General Distillation Framework for Object Detectors via Pearson Correlation Coefficient
https://github.com/open-mmlab/mmrotate
공식 코드MMRotate: A Rotated Object Detection Benchmark using PyTorch
https://github.com/open-mmlab/mmtracking
공식 코드https://github.com/openai/CLIP
공식 코드Learning Transferable Visual Models From Natural Language Supervision
https://github.com/opendr-eu/datasets
공식 코드https://github.com/opendrivelab/bevperception-survey-recipe
공식 코드Geometric-aware Pretraining for Vision-centric 3D Object Detection
https://github.com/opendrivelab/birds-eye-view-perception
공식 코드Leveraging Vision-Centric Multi-Modal Expertise for 3D Object Detection
https://github.com/opengvlab/ego4d-eccv2022-solutions
공식 코드InternVideo-Ego4D: A Pack of Champion Solutions to Ego4D Challenges
https://github.com/opengvlab/hulk
공식 코드Hulk: A Universal Knowledge Translator for Human-Centric Tasks
https://github.com/opengvlab/humanbench
공식 코드Hulk: A Universal Knowledge Translator for Human-Centric Tasks
https://github.com/opengvlab/internimage
공식 코드InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
https://github.com/opengvlab/piip
공식 코드https://github.com/opengvlab/unihcp
공식 코드https://github.com/openmsl/reflection_based_lidar_object_model
공식 코드Simulating Road Spray Effects in Automotive Lidar Sensor Models
https://github.com/openvinotoolkit/training_extensions
공식 코드How to Train an Accurate and Efficient Object Detection Model on Any Dataset
https://github.com/orionbench/orionbench
공식 코드OrionBench: A Benchmark for Chart and Human-Recognizable Object Detection in Infographics
https://github.com/orouskhani/aneurysmdetection
공식 코드nnDetection for Intracranial Aneurysms Detection and Localization
https://github.com/orrzohar/prob
공식 코드PROB: Probabilistic Objectness for Open World Object Detection
https://github.com/oryany12/DeSparsify-Adversarial-Attack
공식 코드DeSparsify: Adversarial Attack Against Token Sparsification Mechanisms in Vision Transformers
https://github.com/oshadajay/CeyMo
공식 코드CeyMo: See More on Roads -- A Novel Benchmark Dataset for Road Marking Detection
https://github.com/oshrout/GraVoS
공식 코드https://github.com/oskyhn/DelftBikes
공식 코드Hallucination In Object Detection -- A Study In Visual Part Verification
https://github.com/osvai/af-dcd
공식 코드Augmentation-Free Dense Contrastive Knowledge Distillation for Efficient Semantic Segmentation
https://github.com/ouyanghaodong/deyo
공식 코드https://github.com/owen-liuyuxuan/visionfactory
공식 코드Scalable Vision-Based 3D Object Detection and Monocular Depth Estimation for Autonomous Driving
https://github.com/ozheng1993/ucf-sst-citysim-dataset
공식 코드CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety Oriented Research and Digital Twins
https://github.com/ozzyou/rp-fem
공식 코드Relational Prior Knowledge Graphs for Detection and Instance Segmentation
https://github.com/paathelb/MEDL-U
공식 코드MEDL-U: Uncertainty-aware 3D Automatic Annotation based on Evidential Deep Learning
https://github.com/pagf188/raxo
공식 코드Superpowering Open-Vocabulary Object Detectors for X-ray Vision
https://github.com/pallgeuer/novic
공식 코드https://github.com/pallgeuer/object_noun_dictionary
공식 코드https://github.com/pandas-team/autonomous-vehicle-environment-perception
공식 코드An intelligent modular real-time vision-based system for environment perception
https://github.com/pangsu0613/CLOCs
공식 코드CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection
https://github.com/pastifra/Continual_Nanodet
공식 코드Latent Distillation for Continual Object Detection at the Edge
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.