Papers with Code 아카이브
Object Counting
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 평가표
- 10
- 보존된 평가 기록
- 69
The goal of Object Counting task is to count the number of object instances in a single image or video sequence. It has many real-world applications such as traffic flow monitoring, crowdedness estimation, and product counting.
Source: Learning to Count Objects with Few Exemplar Annotations
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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https://github.com/Qengineering/YoloV2-ncnn-Jetson-Nano
https://github.com/Qengineering/YoloV2-ncnn-Raspberry-Pi-4
https://github.com/Qengineering/YoloV3-ncnn-Raspberry-Pi-4
https://github.com/RichardMathewsII/YOLBO
https://github.com/Ritik-Sharma38/YoloV3_training_Hands
https://github.com/RobbertBrand/Yolo-Tensorflow-Implementation
https://github.com/Rohed/ml-1
https://github.com/RuoyuChen10/objectdetection-saliency-maps
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/SHU-FLYMAN/Yolov1-TensorFlow
https://github.com/SJTU-Thinklab-Det/r3det-on-mmdetection
https://github.com/SajithMR/Counting_plant_parts
https://github.com/SanketAgrawal/Catagorical-Focal-Loss
https://github.com/SforAiDl/CountCLIP
https://github.com/ShaharrHorn/Detection-Game-logic
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https://github.com/Shraddha2013/darknett
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https://github.com/Shraddha2013/darknetyolo3
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https://github.com/Shraddha2013/myfileyolo
https://github.com/SiHaoShen/Convolutional-Neural-Networks
https://github.com/Singh-sid930/YOLO_pytorch
https://github.com/SinicaGroup/Class-agnostic-Few-shot-Object-Counting
Learning to Count Anything: Reference-less Class-agnostic Counting with Weak Supervision
https://github.com/Stepan-coder/HandWritenSignatureDetection
https://github.com/Stephenfang51/Focal_loss_turtorial
https://github.com/Suci2609/generate_train_list.py-
https://github.com/Sujith2k/darknet
https://github.com/Sushma07/dancedarknet
https://github.com/Techyee/darknet_resource
https://github.com/TheClub4/car-detection-yolov2
https://github.com/TheHidden1/YOLO.JS
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.