https://github.com/MindSpore-paper-code-2/code2/tree/main/ProtoNet
Improving ProtoNet for Few-Shot Video Object Recognition: Winner of ORBIT Challenge 2022
Papers with Code 아카이브
Object Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 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/MindSpore-paper-code-2/code2/tree/main/ResidualAttentionNet
https://github.com/MindSpore-paper-code-3/code2/tree/main/DeepID
https://github.com/MindSpore-paper-code-3/code4/tree/main/googlenet
https://github.com/MindSpore-paper-code-3/code5/tree/main/ResidualAttentionNet
https://github.com/MindSpore-paper-code-3/code8/tree/main/DeepID
https://github.com/MindSpore-scientific-2/code-3/tree/main/ssFPN
https://github.com/MindSpore-scientific/code-5/tree/main/Projected-Distribution
https://github.com/MisaOgura/flashtorch
https://github.com/Mnpr/OC-DeepLearning
https://github.com/Mnpr/Thoractic-Diseases-Detection
https://github.com/Mnpr/Thoractic_Disease_Detection
https://github.com/MrRiahi/Convolutional-Neural-Networks-Tensorflow
https://github.com/Mungosin/Mozgalo
https://github.com/NUAAXQ/MLCVNet
https://github.com/NYU-DICE-Lab/open_clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/Naghipourfar/Deep-Clustering
Human-like Clustering with Deep Convolutional Neural Networks
https://github.com/NisTa24/ReNet-Implementation
ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks
https://github.com/OML-Team/open-metric-learning
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/Oichii/DeepPulse-pytorch
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond
https://github.com/Oishani/Machine-Learning-Projects
https://github.com/OzHsu23/chineseocr
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images
https://github.com/Pacific-cyber/KUKA_Catch_Project
SegICP: Integrated Deep Semantic Segmentation and Pose Estimation
https://github.com/PaddlePaddle/PASSL/blob/main/docs/Train_CLIP_model.md
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/PaddlePaddle/PaddleClas
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/PaddlePaddle/PaddleSeg
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond
https://github.com/PaddlePaddle/PaddleYOLO
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
https://github.com/Pangoraw/GeoSSL
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/Paperspace/hyperopt-keras-sample
https://github.com/PhucDuy/FoodDetector
FoodTracker: A Real-time Food Detection Mobile Application by Deep Convolutional Neural Networks
https://github.com/PhucDuy/MyKcal
FoodTracker: A Real-time Food Detection Mobile Application by Deep Convolutional Neural Networks
https://github.com/PistonY/ResidualAttentionNetwork
https://github.com/Piyushdharkar/Residual-Attention-Aware-Network
https://github.com/PointsCoder/VOTR
https://github.com/Puning97/AUTO-for-OOD-detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.