Deploy your Flask web app classifier on Heroku which is written using fastai library.
-
Updated
Nov 19, 2019 - HTML
Deploy your Flask web app classifier on Heroku which is written using fastai library.
Demo of Face Recognition web service
We use pretrained networks VGGnet, AlexNet, GoogLeNet, ResNet which trained on the ImageNet dataset as a feature extractor to classify images.
Detect objects from a webcam in your browser with Cloudflare Workers AI!
Explainable Speaker Recognition system
Traffic sign classification using Resnet deep network
Image Style Recognition using Transfer Learning with Pre-trained ResNet
The system in this research uses transfer learning to categorise the many forms of weather. These weather conditions are primarily divided into 5 groups: cloudy, sunny, rainy, foggy, and sunrise. High-performance classifiers in artificial intelligence (AI) mostly use deep-learning (DL) techniques.
Predict the apparent age of a person possibly in real time. The model used is pre-trained on VGG16 architecture and then trained using a convolutional neural network on the ChaLearn LAP dataset which consists of 8000 image samples. Test Accuracy achieved – 84%.
A web-based animal classification system using Zero-Shot Learning and ResNet, built with Flask and PyTorch. This application can classify animals in images without being explicitly trained on them, using semantic embeddings to make predictions.
🐮 CUAI Deep Learning Study Materials (2020.01 - 2020.03) 🧠
Interface for deployed CNN Image Classifier
Project Website for the Deep Learning Online Seminar at University
Add a description, image, and links to the resnet topic page so that developers can more easily learn about it.
To associate your repository with the resnet topic, visit your repo's landing page and select "manage topics."