https://github.com/xiaoqi-zhao-dlut/x-ray-pbd
공식 코드Towards Automatic Power Battery Detection: New Challenge, Benchmark Dataset and Baseline
Papers with Code 아카이브
Crowd Counting
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Crowd Counting is a task to count people in image. It is mainly used in real-life for automated public monitoring such as surveillance and traffic control. Different from object detection, Crowd Counting aims at recognizing arbitrarily sized targets in various situations including sparse and cluttering scenes at the same time.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xiyang1012/Local-Crowd-Counting
공식 코드Adaptive Mixture Regression Network with Local Counting Map for Crowd Counting
https://github.com/yankeegsj/bla
공식 코드https://github.com/yeyimilk/crowdvlm-r1
공식 코드https://github.com/yeyimilk/deep-learning-for-manatee-counting
공식 코드Counting Manatee Aggregations using Deep Neural Networks and Anisotropic Gaussian Kernel
https://github.com/yiming-m/ebc-zip
공식 코드EBC-ZIP: Improving Blockwise Crowd Counting with Zero-Inflated Poisson Regression
https://github.com/yimingma/crowdcounting-icc
공식 코드Inception-Based Crowd Counting -- Being Fast while Remaining Accurate
https://github.com/zebrabeast/DenseTrack-
공식 코드DenseTrack: Drone-based Crowd Tracking via Density-aware Motion-appearance Synergy
https://github.com/zhaoyi-yan/dkpnet
공식 코드https://github.com/zhiqic/crowdcount-stackpool
공식 코드Stacked Pooling: Improving Crowd Counting by Boosting Scale Invariance
https://github.com/zhiqic/rethinking-counting
공식 코드Rethinking Spatial Invariance of Convolutional Networks for Object Counting
https://github.com/zhiyuanyou/SAFECount
공식 코드Few-shot Object Counting with Similarity-Aware Feature Enhancement
https://github.com/zz-zik/tapnet
공식 코드Transformer-Based Dual-Optical Attention Fusion Crowd Head Point Counting and Localization Network
https://github.com/0492wzl/tensorflow_slim_densenet
https://github.com/0bserver07/Keras-SegNet-Basic
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/0jason000/DenseNet
https://github.com/2023-MindSpore-1/ms-code-146
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/3D_DenseNet
https://github.com/2023-MindSpore-1/ms-code-177
https://github.com/2023-MindSpore-1/ms-code-18/tree/main/lresnet100e_ir
https://github.com/2023-MindSpore-1/ms-code-201
https://github.com/2023-MindSpore-1/ms-code-208
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/FaceAttribute
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/resnet50_adv_pruning
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssd_resnet34
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssd_resnet_34
https://github.com/2023-MindSpore-1/ms-code-22
https://github.com/2023-MindSpore-4/Code-5/tree/main/lresnet100e_ir
https://github.com/2023-MindSpore-4/Code10/tree/main/ResNet
https://github.com/2023-MindSpore-4/Code11/tree/main/resnet50_adv_pruning
https://github.com/2023-MindSpore-4/Code11/tree/main/ssc_resnet50
https://github.com/2023-MindSpore-4/Code11/tree/main/ssd_resnet_34
https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_resnet34
https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_resnet_34
https://github.com/2023-MindSpore-4/Code9/tree/main/ResNet
https://github.com/2024-MindSpore-1/Code6/tree/main/3D_DenseNet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.