Sushi Dish - Object detection and classification from real images

餐盘中的菜品检测：用于自动饮食记录与营养管理的框架

Dish detection in food platters: A framework for automated diet logging and nutrition management

提出一个端到端饮食管理框架，通过比较深度学习模型，在68,005张印度餐盘图像上实现菜品检测与多标签分类，YOLOv8x（mAP=87.70%）和ResNet152（mAP=84.51%）分别最优，并开发了移动应用原型。

协同学习：面向路侧相机的半监督目标检测

Co-Learning: Towards Semi-Supervised Object Detection with Road-side Cameras

针对路侧相机目标检测中标注数据获取难的问题，提出基于师生架构的半监督学习框架Co-Learning，通过互学习和标注对齐策略，仅用10%标注数据就实现了与全监督方案相当的性能。

A Petri Dish for Histopathology Image Analysis

With the rise of deep learning, there has been increased interest in using neural networks for histopathology image analysis, a field that investigates the properties of biopsy or resected specimens traditionally manually examined under a microscope by pathologists. However, challenges such as limited data, costly annotation, and processing high-resolution and variable-size images make it difficult to quickly iterate over model designs. Throughout scientific history, many significant research directions have leveraged small-scale experimental setups as petri dishes to efficiently evaluate expl…

AGAR a microbial colony dataset for deep learning detection

The Annotated Germs for Automated Recognition (AGAR) dataset is an image database of microbial colonies cultured on agar plates. It contains 18000 photos of five different microorganisms as single or mixed cultures, taken under diverse lighting conditions with two different cameras. All the images are classified into "countable", "uncountable", and "empty", with the "countable" class labeled by microbiologists with colony location and species identification (336442 colonies in total). This study describes the dataset itself and the process of its development. In the second part, the performanc…

Fine-grained Image Classification by Exploring Bipartite-Graph Labels

Given a food image, can a fine-grained object recognition engine tell "which restaurant which dish" the food belongs to? Such ultra-fine grained image recognition is the key for many applications like search by images, but it is very challenging because it needs to discern subtle difference between classes while dealing with the scarcity of training data. Fortunately, the ultra-fine granularity naturally brings rich relationships among object classes. This paper proposes a novel approach to exploit the rich relationships through bipartite-graph labels (BGL). We show how to model BGL in an over…

FoodLogAthl-218: 通过饮食管理应用构建一个现实世界的食物图像数据集

FoodLogAthl-218: Constructing a Real-World Food Image Dataset Using Dietary Management Applications

FoodLogAthl-218数据集通过用户真实餐食照片构建，包含218类食物图像及丰富元数据，提出基于上下文的分类任务和增量微调协议，用于提升饮食管理应用的图像分类性能。