We use pretrained networks VGGnet, AlexNet, GoogLeNet, ResNet which trained on the ImageNet dataset as a feature extractor to classify images.
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Jul 16, 2018 - HTML
We use pretrained networks VGGnet, AlexNet, GoogLeNet, ResNet which trained on the ImageNet dataset as a feature extractor to classify images.
use transfer learning to detect smoke in images and videos
This repository contains code and bonus content which will be added from time to time for the books "Learning Generative Adversarial Network- GAN" and "R Data Analysis Cookbook - 2nd Edition" by Packt
ISIC 2019 - Skin Lesion Analysis Towards Melanoma Detection
Determine a seedling Species from an image based on Kaggle Competition
Fill-in-the-BERT uses pre-trained BERT Masked Language Model for Infering the task of fill in the blanks.
Classifying between two folk dance forms of India using Deep Learning
An autoML for explainable text classification.
Image Classification Model using Transfer-Learning (MobileNetV2) running on Google App Engine.
Interface for Image-Related Deep Learning Models (E.g. NSFW, MAYBE and SFW)
Code for the paper Transfer learning for the probabilistic classification vector machine
Convolutional Neural Networks (CNN) project! In this project, you will find how to build a pipeline to process real-world, user-supplied images
Dog Breed Classification Project which classifies real world dog images and predicts their breed out of 133 breeds/classes. If supplied an image of a human face, the code will identify the resembling dog breed.
Classifying Flower Images with a custom CNN
Rice Leaf Disease Prediction System using Deep Learning, specifically leveraging the blessings of transfer learning.
A dog breed classifier made using Convolutional Neural Networks and Transfer Learning, as a part of the Udacity's Machine Learning Engineer Nanodegree.
Apna Doctor is an AI based Health Check-Up Web Application
This repository offers a robust solution for multilabel image classification. Utilizing advanced neural networks like VGG16, VGG19, ResNet50, InceptionV3, DenseNet121, and MobileNetV2, the project achieves precise classification across 107 diverse categories.
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