Papers with Code 아카이브

Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

39

평가표

124

보존된 평가 기록

977

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/pessimiss/ai100-w8-master

SSD: Single Shot MultiBox Detector

https://github.com/peter8301120/darknet_t

YOLO9000: Better, Faster, Stronger

https://github.com/peteryuX/arcface-tf2

Deep Residual Learning for Image Recognition

https://github.com/peteryuX/tensorflow-GHM-loss

Gradient Harmonized Single-stage Detector

https://github.com/petko-nikolov/pysemseg

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/petrum01/Capstone_project_object_detection

Focal Loss for Dense Object Detection

https://github.com/petuum/adaptdl

AdaScale SGD: A User-Friendly Algorithm for Distributed Training

https://github.com/pgarz/runway_exercise

DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks

https://github.com/phamquiluan/table-transformer

PubTables-1M: Towards comprehensive table extraction from unstructured documents

https://github.com/philip-bachman/amdim-public

Data-Efficient Image Recognition with Contrastive Predictive Coding

https://github.com/philippdahlinger/ltsgns_ai4science

Container: Context Aggregation Network

https://github.com/philipperemy/keras-frn

Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks

https://github.com/phillity/BioCapsule

Deep Residual Learning for Image Recognition

https://github.com/phnk/yolov3

YOLOv3: An Incremental Improvement

https://github.com/phtruongan/py-faster-rcnn-docker

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

https://github.com/phykn/film-defect-detection

Mask R-CNN

https://github.com/piaosonglin1985/tf-faster-rcnn

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

https://github.com/picsart-ai-research/videoinr-continuous-space-time-super-resolution

Deformable ConvNets v2: More Deformable, Better Results

https://github.com/pideyi1025/DeepLabV3Plus-RailSem19

Rethinking BiSeNet For Real-time Semantic Segmentation

https://github.com/pierlj/proto_faster_rcnn

Experience feedback using Representation Learning for Few-Shot Object Detection on Aerial Images

https://github.com/pierlj/pycocosiou

A Normalized Gaussian Wasserstein Distance for Tiny Object Detection

https://github.com/pierluigiferrari/ssd_keras

SSD: Single Shot MultiBox Detector

https://github.com/pierrebaque/DeepOcclusion

Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection

https://github.com/pietz/unet-keras

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/pikabite/segmentations_tf2

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/pikkaay/efficientnet_gpu

Deep Residual Learning for Image Recognition

https://github.com/piotrplata/squeeze_excitation_keras

Squeeze-and-Excitation Networks

https://github.com/pirate0109/UNet_in_Pytorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/pistachio0812/YOLOX

YOLOX: Exceeding YOLO Series in 2021

https://github.com/pixeli99/svd_xtend

TrackDiffusion: Tracklet-Conditioned Video Generation via Diffusion Models

https://github.com/pixiedust18/darknet_mask_classification

YOLOv3: An Incremental Improvement

https://github.com/pj1920/mask-r-cnn

Mask R-CNN

https://github.com/pjlab-adg/openpcseg

Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution

https://github.com/pjlab-adg/pcseg

Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution

https://github.com/pjreddie/darknet

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/pku-dair/hetu

PointCLIP: Point Cloud Understanding by CLIP

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.