https://github.com/explosion5/Dolphins
공식 코드DOLPHINS: Dataset for Collaborative Perception enabled Harmonious and Interconnected Self-driving
Papers with Code 아카이브
2D Object Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/fabiankueppers/calibration-framework
공식 코드https://github.com/fabienbaradel/object_level_visual_reasoning
공식 코드https://github.com/fabiencode/lg3d
공식 코드https://github.com/fabiopoiesi/monet_dataset
공식 코드The MONET dataset: Multimodal drone thermal dataset recorded in rural scenarios
https://github.com/facebookresearch/ConvNeXt
공식 코드https://github.com/facebookresearch/DepthContrast
공식 코드Self-Supervised Pretraining of 3D Features on any Point-Cloud
https://github.com/facebookresearch/Detic
공식 코드Detecting Twenty-thousand Classes using Image-level Supervision
https://github.com/facebookresearch/PLRC
공식 코드Point-Level Region Contrast for Object Detection Pre-Training
https://github.com/facebookresearch/SlowFast
공식 코드MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
https://github.com/facebookresearch/adaptive_teacher
공식 코드https://github.com/facebookresearch/barlowtwins
공식 코드Barlow Twins: Self-Supervised Learning via Redundancy Reduction
https://github.com/facebookresearch/convnext-v2
공식 코드ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
https://github.com/facebookresearch/cutler
공식 코드Cut and Learn for Unsupervised Object Detection and Instance Segmentation
https://github.com/facebookresearch/deit
공식 코드Augmenting Convolutional networks with attention-based aggregation
https://github.com/facebookresearch/detectron
공식 코드https://github.com/facebookresearch/detectron2
공식 코드https://github.com/facebookresearch/detectron2/tree/main/projects/MViTv2
공식 코드MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
https://github.com/facebookresearch/detectron2/tree/main/projects/ViTDet
공식 코드Exploring Plain Vision Transformer Backbones for Object Detection
https://github.com/facebookresearch/detectron2/tree/master/projects/TensorMask
공식 코드https://github.com/facebookresearch/detr
공식 코드https://github.com/facebookresearch/dino
공식 코드https://github.com/facebookresearch/efm3d
공식 코드EFM3D: A Benchmark for Measuring Progress Towards 3D Egocentric Foundation Models
https://github.com/facebookresearch/egoobjects
공식 코드EgoObjects: A Large-Scale Egocentric Dataset for Fine-Grained Object Understanding
https://github.com/facebookresearch/fair_self_supervision_benchmark
공식 코드Scaling and Benchmarking Self-Supervised Visual Representation Learning
https://github.com/facebookresearch/hiera
공식 코드Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
https://github.com/facebookresearch/imvotenet
공식 코드ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes
https://github.com/facebookresearch/long_seq_mae
공식 코드https://github.com/facebookresearch/mae
공식 코드https://github.com/facebookresearch/maws
공식 코드The effectiveness of MAE pre-pretraining for billion-scale pretraining
https://github.com/facebookresearch/mmf/tree/main/projects/unit
공식 코드UniT: Multimodal Multitask Learning with a Unified Transformer
https://github.com/facebookresearch/mvit
공식 코드MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
https://github.com/facebookresearch/nerf-det
공식 코드NeRF-Det: Learning Geometry-Aware Volumetric Representation for Multi-View 3D Object Detection
https://github.com/facebookresearch/omni3d
공식 코드Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild
https://github.com/facebookresearch/paco
공식 코드https://github.com/facebookresearch/projectaria_tools
공식 코드Aria Digital Twin: A New Benchmark Dataset for Egocentric 3D Machine Perception
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.