WitrynaiMaterialist Fashion Kaggle Competition at FGVC Download. We have hosted Kaggle challenge (iMat-Fashion) using Fashionpedia dataset under FGVC (Fine-Grained … Witryna1 cze 2016 · iMaterialist Fashion Challenge(FGVC5) Mar 2024 Worked on automatic product recognition in Fashion images (1 million+ images and 250+ labels ) with each image having multi-labels. Ranked 69th out of 200+ participants on Kaggle Private Leaderboard. For more details, please visit my kaggle profile
吴文滔 - Udacity - 中国 福建省 福州 LinkedIn
WitrynaiMaterialist (Fashion) 2024 at FGVC6 Fine-grained segmentation task for fashion and apparel Finished 36 out of 242 (Top 15%) In iMaterialist (Fashion) 2024 at FGVC6 Challenge Kagglers are asked do build a model that accurately segments and classifies fashion objects in images of daily-life, celebrity events, and online shopping. Witrynaest, especially in fashion domain [18, 12, 4]. In light of this, we introduce an iMaterialist Fashion Attribute Dataset (iFashion), which includes over one million annotated fash-ion images where the labels are curated by fashion experts. The label space includes 8 groups and a total of 228 fashion attributes, as described in Table 1. css backdrop filter firefox
Fashionpedia - GitHub Pages
WitrynaCompared to the COCO Dataset, the iMaterialist Dataset is closer to a real-world application with a variety of objects resembling clothing apparels. iMaterialist also boasts of a much better labeling quality and is more fine-grained, so the general contour of the masked objects is of high quality and well preserved. Witryna现就读于福州大学机械工程专业研三，研究冗余机械臂逆运动学算法研究。本人擅长图像识别深度学习算法，使用深度学习独自一人参加kaggle及天池的图像多分类和图像多标签大赛，完成 Udacity《机器人软件工程》课程，包括机械臂的控制，视觉处理、ROS 系统、深度学习、轨迹规划等项目。 WitrynaDeveloped an image classifier for imaterialist-challenge-fashion-2024 dataset on Kaggle using fastai library and implemented basic concepts of deep learning. Driving style analysis using real time computer vision and neural networks Aug 2024 - Nov 2024. The aim of this project was to reduce the number of road accidents by detecting … css backdrop-filter: blur 5px
