Papers with Code 아카이브
Object Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 9
- 보존된 평가 기록
- 31
Object recognition is a computer vision technique for detecting + classifying objects in images or videos. Since this is a combined task of object detection plus image classification, the state-of-the-art tables are recorded for each component task here and here.
<span style="color:grey; opacity: 0.6">( Image credit: Tensorflow Object Detection API )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yangboz/TransferLearning4Dentist
https://github.com/yangyi02/3d_pose_fine_grained
Improving Annotation for 3D Pose Dataset of Fine-Grained Object Categories
https://github.com/yangyi02/finegrained-pose
Improving Annotation for 3D Pose Dataset of Fine-Grained Object Categories
https://github.com/yangyucheng000/densenet/blob/main/densenet.py
https://github.com/yangyucheng000/ms_cv/tree/main/SPPNet
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
https://github.com/yaoyao-liu/meta-transfer-learning
https://github.com/yaoyao-liu/mini-imagenet-tools
https://github.com/yashdani/Object-Detection-Using-YOLOv3-Algorithm
https://github.com/yhenon/keras-spp
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
https://github.com/yichigo/Chest-X-Ray
https://github.com/ylqi/count-anything
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/yonghyun-ahn/line-out-of-distribution-detection-by-leveraging-important-neurons
https://github.com/yoojungsun0/Psych239
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/yoshitomo-matsubara/torchdistill
https://github.com/yueruchen/sppnet-pytorch
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
https://github.com/yuhsijen/Object-Detector
https://github.com/yui-mhcp/base_dl_project
https://github.com/yusufsugiono/klasifikasi-gambar-bunga
https://github.com/yuuun/clip_pytorch
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/yuyi1005/point2rbox-mmrotate
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
https://github.com/yw0nam/DenseNet
https://github.com/yxb-nku/strip-r-cnn
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
https://github.com/yzhou359/MakeItTalk
SceneGraphNet: Neural Message Passing for 3D Indoor Scene Augmentation
https://github.com/zahra-bk/MLOps_VideoAnomalyDetection
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
https://github.com/zck119/3dgan-release
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
https://github.com/zhanghuiyao/yolov5_mindspore
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
https://github.com/zhangxu0963/npc
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/zhihaozhang97/ru-ai
https://github.com/zhirongw/3DShapeNets
https://github.com/zhuolingli/FewVS
https://github.com/zhusiling/GCNet
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond
https://github.com/zhxfl/CUDA-CNN
https://github.com/zjZSTU/GoogLeNet
https://github.com/zjZSTU/ResNet
https://github.com/zjunet/dropmessage
Improving neural networks by preventing co-adaptation of feature detectors
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.