Tuning different Pytorch/Tensorflow pre-trained models like ResNet50 , Wide ResNet_50.2 , VGG16 and a custom CNN model to classify a dog image among 120 breeds
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Updated
Dec 5, 2021 - Jupyter Notebook
Tuning different Pytorch/Tensorflow pre-trained models like ResNet50 , Wide ResNet_50.2 , VGG16 and a custom CNN model to classify a dog image among 120 breeds
ML + MobileApp = JaDIS
Official implementation of the paper: Learn to aggregate global and local representations for few-shot learning
Stanford dogs dataset breed classification with Xception (CNN)
FGVC project with the Stanford Dogs dataset.
Applying Transfer Learning on Stanford Dogs Dataset
The source code for Multi-Scale Kronecker-Product Relation Networks for Few-Shot Learning
This repository performs Computer-Vision tasks on multiple Image Datasets using CNN based Networks.
Dogs Breeds Classification With TFLite Using Stanford Dogs Breeds Dataset.
Utilizing CNNs for image classification of 120 dog breeds in the Stanford Dogs Dataset.
This project is a study that compares two machine learning approches (Feature extraction with SIFT descriptors and deep learning) in order to classify dogs races (Stanford dogs dataset). A flask application is built from the weights of the final deep neural network trained : http://bit.ly/mk_cv_dogs
Using transfer learning on a CNN to build a 120 breed dog classifier
"Exploring Vision Transformers for Fine-grained Classification" at CVPRW FGVC8
kaggle Dog Breed Identification
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