There are 5 repositories under multi-label-image-classification topic.
Awesome Multi-label Image Recognition Paper List
A Baseline for Multi-Label Image Classification Using Ensemble Deep CNN
A paper list of awesome Image Tagging
LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognition
[ECCV 2022] Offical implementation of the paper "Acknowledging the Unknown for Multi-label Learning with Single Positive Labels".
Multi-Disease Detection in Retinal Imaging based on Ensembling Heterogeneous Deep Learning Models
Multi-label Classification using PyTorch on the CelebA dataset.
Keras implementation of multi-label classification of movie genres from IMDB posters
Awesome Multi-label Image Recognition Paper List
A multi-label-classification model for common thorax disease.
A rule-based algorithm enabled the automatic extraction of disease labels from tens of thousands of radiology reports. These weak labels were used to create deep learning models to classify multiple diseases for three different organ systems in body CT.
Multi-label-binary-classification model for multi-label-weather-prediction problem
Multi-label image classification for movie posters by adopting deep neural network architecture.
Multi-label image recognition with Graph Convolution Network and its variants
Kaggle Human Protein Atlas Image Classification Challenge (top 7%)
A Tensorflow ConvNet Approach to the Multi Label Genre Classification on Movie Posters
2021년 2학기 디자인적 사고 - 케어 라벨 인식 프로젝트
Multi-Label Image Classifier using Tensorflow on images dataset which has six classes i.e, buildings, forest, glacier, mountain, sea, street
The project was part of our syllabus on the IPCVai EMJM programme, which is a collaboration between UAM , PPCU and UBx.
An easy-to-use multi-label image dataset generator.
The repository contains three deep learning models created for Kaggle and PASCAL datasets.
[BoostcampAI Tech 6기] 마스크 착용 여부, 성별, 연령을 판단하는 Task
A Tensorflow Implementation of Multi-Modal-Multi-Scale Image Annotation (Not Author Code)
Predicting protein organelle localization in a diverse dataset using multi-label classification and mean F1-score evaluation, as part of the Machine Learning for Life Sciences course at Ghent University