https://github.com/CSSEHealthcare/InverseLearning
공식 코드An Open-Source Dataset on Dietary Behaviors and DASH Eating Plan Optimization Constraints
Papers with Code 아카이브
Nutrition
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/GCVCG/VolETA-MetaFood
공식 코드https://github.com/LARC-CMU-SMU/ACME
공식 코드Learning Cross-Modal Embeddings with Adversarial Networks for Cooking Recipes and Food Images
https://github.com/MUSEFood/MUSEFood
공식 코드MUSEFood: Multi-sensor-based Food Volume Estimation on Smartphones
https://github.com/SamanKhamesian/NutriGen
공식 코드https://github.com/WenjieWWJ/FoodRec
공식 코드https://github.com/aametwally/LearningFoodPreferences
공식 코드https://github.com/atulkum/pointer_summarizer
공식 코드https://github.com/bidalab/ai4food-nutritiondb
공식 코드https://github.com/bidalab/ai4food-nutritionfw
공식 코드AI4Food-NutritionFW: A Novel Framework for the Automatic Synthesis and Analysis of Eating Behaviours
https://github.com/chishengchen/resvmamba
공식 코드https://github.com/darsh10/Nutribullets
공식 코드Nutri-bullets: Summarizing Health Studies by Composing Segments
https://github.com/gcvcg/voltex
공식 코드VolTex: Food Volume Estimation using Text-Guided Segmentation and Neural Surface Reconstruction
https://github.com/google-research-datasets/Nutrition5k
공식 코드Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food
https://github.com/hugochan/PFoodReq
공식 코드https://github.com/ihealthlab/dietitianexameval
공식 코드https://github.com/in-vivo-group/nhanes-gcp
공식 코드https://github.com/jasonzhangzy1757/diet-odin
공식 코드Diet-ODIN: A Novel Framework for Opioid Misuse Detection with Interpretable Dietary Patterns
https://github.com/jdariasl/morbo
공식 코드Swine Diet Design using Multi-objective Regionalized Bayesian Optimization
https://github.com/kanak8278/dialogkperm
공식 코드https://github.com/komeijiforce/cuckoo
공식 코드Cuckoo: An IE Free Rider Hatched by Massive Nutrition in LLM's Nest
https://github.com/lancezpf/kefs
공식 코드Synthesizing Knowledge-enhanced Features for Real-world Zero-shot Food Detection
https://github.com/m3rg-iitd/discomat
공식 코드DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles
https://github.com/nfdi4health/vambn-extensions-evaluations
공식 코드https://github.com/ostadabbas/nns-detection-and-segmentation
공식 코드https://github.com/revathyramanan/cooking-action-generation
공식 코드Cook-Gen: Robust Generative Modeling of Cooking Actions from Recipes
https://github.com/sciencenlp-lab/dimb-re
공식 코드DiMB-RE: Mining the Scientific Literature for Diet-Microbiome Associations
https://github.com/teosusnjak/survival-analysis-stage1
공식 코드https://github.com/yuliang-liu/multimodalocr
공식 코드OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models
https://github.com/CSSEHealthcare/Dietary-Behavior-Dataset
An Open-Source Dataset on Dietary Behaviors and DASH Eating Plan Optimization Constraints
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/allenai/dolma
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
https://github.com/bramiozo/PubScience
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
https://github.com/hwang1996/ACME
Learning Cross-Modal Embeddings with Adversarial Networks for Cooking Recipes and Food Images
https://github.com/rvlopes/gloria
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.