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/imoonlab/hyper-yolo
https://github.com/imoonlab/yolov13
https://github.com/insigh/Relation_Network_for_Objection
https://github.com/inzouzouwetrust/IMA_RWCNN_project
https://github.com/is2ai/kazparc
https://github.com/isaaccorley/resize-is-all-you-need
https://github.com/isrugeek/robotics_recognition
Multimodal Deep Learning for Robust RGB-D Object Recognition
https://github.com/j9650/CoDeepNEAT
https://github.com/jaekyeom/mabas
https://github.com/jaychempan/LAE-DINO
https://github.com/jbernoz/deeppolyp
https://github.com/jd730/OICR-pytorch
Multiple Instance Detection Network with Online Instance Classifier Refinement
https://github.com/jetpacapp/DeepBeliefSDK
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
https://github.com/jhaprince/multibully
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/jhon-zelada/proyect
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
https://github.com/jimheaton/Ultra96_ML_Embedded_Workshop
https://github.com/jimzai/mode-ood
https://github.com/jiportilla/edge-max-object-detector
https://github.com/jiweibo/imagenet
https://github.com/jmesich/chestxray
https://github.com/joaopauloschuler/neural-api
Multi-path Convolutional Neural Networks for Complex Image Classification
https://github.com/joeylitalien/noise2noise-pytorch
https://github.com/johangenis/TF2.0-Advanced-Transfer-Learning-Project-1
https://github.com/john-mlr/CIFAR-10-SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/johntd54/stanford_car
https://github.com/jonnor/datascience-master
https://github.com/jramapuram/SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/justinessert/hierarchical-deep-cnn
HD-CNN: Hierarchical Deep Convolutional Neural Network for Large Scale Visual Recognition
https://github.com/jwcalder/GraphLearningLayer
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/jylins/core-text
https://github.com/k0pch4/big-little-net
Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition
https://github.com/kahnchana/RNN
Improving neural networks by preventing co-adaptation of feature detectors
https://github.com/kaishengtai/neuralart
https://github.com/kakaobrain/rq-vae-transformer
DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models
https://github.com/kaseris/ILSVRCPlus
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.