brainymachines / fgvc5-cvpr2018-food-fashion
FGVC, Fine Grained Visual Categorization
From organization brainymachines
FGVC, Fine Grained Visual Categorization
From organization brainymachines
FGVC, Pytorch implementation of "Fine-grained Visual Classification with High-temperature Refinement and Background Suppression"
From user chou141253
FGVC, Pytorch implementation for "A Novel Plug-in Module for Fine-Grained Visual Classification". fine-grained visual classification task.
From user chou141253
FGVC, A PyTorch implementation of WS-DAN (Weakly Supervised Data Augmentation Network) for FGVC (Fine-Grained Visual Classification)
From user guyuc
FGVC, Cassava disease prediction for the FGVCx competition of CVPR
From organization icassava
FGVC, PFNet: A Novel Part Fusion Network for Fine-grained Visual Categorization
From user jingyunliang
FGVC, Cassava disease classification using architectures such as ResNet50, MobileNet, VGG16, and SE-ResNeXt.
From user kwantommy
FGVC, PyTorch custom dataset APIs -- CUB-200-2011, Stanford Dogs, Stanford Cars, FGVC Aircraft, NABirds, Tiny ImageNet, iNaturalist2017
From user lvyilin
FGVC, Repo for the course project of 11785 Introduction to Deep Learning
From user maituoy
FGVC, Self-Supervised Learning for Fine-Grained Image Categorization
From user mmaaz60
FGVC, MXNet finetune baseline (res152) for iNaturalist Challenge at FGVC 2017
From user phunterlau
FGVC, Code release for Your “An Erudite Fine-Grained Visual Classification Model (CVPR 2023)"
From organization pris-cv
FGVC, Code release for Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches (ECCV2020)
From organization pris-cv
FGVC, [ICCV 2023] Learning Fine-Grained Features for Pixel-wise Video Correspondences
From user qianduoduolr
FGVC, This repository is for Kaggle Competetion Plant Pathology 2020 - FGVC7. And our rank is 5/1115.
From user ruisongzhou
FGVC, https://www.kaggle.com/competitions/sorghum-id-fgvc-9
From user sisuolv
FGVC, Objectives of ‘Plant Pathology Challenge’ are to train a model using images of training dataset to 1) Accurately classify a given image from testing dataset into different diseased category or a healthy leaf; 2) Accurately distinguish between many diseases, sometimes more than one on a single leaf; 3) Deal with rare classes and novel symptoms; 4) Address depth perception—angle, light, shade, physiological age of the leaf; and 5) Incorporate expert knowledge in identification, annotation, quantification, and guiding computer vision to search for relevant features during learning.
From user victorojewale
FGVC, FGVCx Fungi Classfiication competition details
From organization visipedia
FGVC, Top 3% (7/336) solution for iWildCam 2019 - FGVC6 competition (Categorize animals in the wild)
From user walleclipse
FGVC, Code for Fast Bird Part Localization (FGVC 2015)
From user yassersouri
Home Page: http://yassersouri.github.io/pages/fast-bird-part.html
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