https://github.com/UCB-ICSI-Vision-Group/decaf-release
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
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/UCBAIR/decaf-release
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
https://github.com/UCMerced-ML/LC-model-compression
https://github.com/UESBTC/DenseNet-Tensorflow
https://github.com/V3Det/mmdetection-V3Det
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
https://github.com/VadymV/ICOS
Are we done with object recognition? The iCub robot's perspective
https://github.com/Vamshi399/CarND-Vehicle-Detection
https://github.com/VandyCatLab/idvor
https://github.com/VinayBN8997/CIFAR-10-object-detection
https://github.com/ViswanathaReddyGajjala/FewShotWithoutForgetting
https://github.com/Vooban/Hyperopt-Keras-CNN-CIFAR-100
https://github.com/Westlake-AI/openmixup
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/XG293/SupConLoss
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/Xephyrum/Agriculturice
https://github.com/Xephyrum/CropsMD
https://github.com/YeLyuUT/UAVidToolKit
https://github.com/Yeeun-Lee/parrot_proj1
https://github.com/Yeeun-Lee/parrot_vision_competition
https://github.com/YerevaNN/DIIN-in-Keras
https://github.com/YixuanLi/densenet-tensorflow
https://github.com/YvanG/VQGAN-CLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/ZF4444/MMAL-Net
Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization
https://github.com/ZackPashkin/text2cartoon-pytorch-CLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/ZdenekStraka/precnet
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
https://github.com/a736875071/clip-vit-large-patch14
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/abhasahay/Object-Recognition-using-Deep-Learning
https://github.com/abhijain864/Project_2020_May-July
Adapting Deep Network Features to Capture Psychological Representations
https://github.com/abhijain864/Project_Comparing_neural-nets-with-pyschological-representations-
Adapting Deep Network Features to Capture Psychological Representations
https://github.com/abhijain864/Summer_Project_2020
Adapting Deep Network Features to Capture Psychological Representations
https://github.com/acholston/SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/addisonklinke/pytorch-architectures/blob/master/torcharch/conv.py
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
https://github.com/ae-foster/pytorch-simclr
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/agassi4013/Log-DenseNet-Tensorflow
https://github.com/ahmedssabir/textual-visual-semantic-dataset
https://github.com/ai-forever/ru-clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/aimbsg/EVA4_S15
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.