Feature extraction using Keras with the VGG, Inception and ResNet architectures
-
Updated
Jul 20, 2019 - Python
Feature extraction using Keras with the VGG, Inception and ResNet architectures
A model inspired by inception v1 for classification of bird species
This repository contains the implementation of the Inception model from scratch and the pretrained V3 model, both used on the flower dataset.
This study focuses on four deep-learning models, which are Inception V3, MobileNet V2, ResNet152V2, and VGG19, aiming to enhance the accuracy of tumor Classification
Creating a Sequential CNN model to classify images of various datasets and comparing the results to pretrained models (VGG16 and Inception V3). A dashboard design for the CNN model for the prediction
Multi-class Segmentation Examples with U-Net.
This implements training of Deep NU-InNet from Accuracy improvement of Thai food image recognition using deep convolutional neural networks by Chakkrit Termritthikun and Surachet Kanprachar.
Attempts to solve a Kaggle competition using a convolutional neural network in tensorflow with the inception architecture.
Classify gender based on face image.
Deep Learning Implementations
This repository is based on a project completed as part of the Deep Learning Specialization on Coursera by DeepLearning.AI.
Whales swimming in the beautiful world of the localhosts
Face recognition system using FaceNet and OpenCV.
We are going to use inception v3 for mobile manufacture image based classification.
Localize and as well as recognize the faces in a real-time as well as static images
Add a description, image, and links to the inception topic page so that developers can more easily learn about it.
To associate your repository with the inception topic, visit your repo's landing page and select "manage topics."