Fast and accurate AI powered file content types detection
-
Updated
Apr 20, 2026 - Python
Fast and accurate AI powered file content types detection
Collection of Keras models used for classification
Keras implementation of a ResNet-CAM model
Distributed Keras Engine, Make Keras faster with only one line of code.
K-CAI NEURAL API - Keras based neural network API that will allow you to create parameter-efficient, memory-efficient, flops-efficient multipath models with new layer types. There are plenty of examples and documentation.
Object classification with CIFAR-10 using transfer learning
CΓ³digos Python com diferentes aplicaΓ§Γ΅es como tΓ©cnicas de machine learning e deep learning, fundamentos de estatΓstica, problemas de regressΓ£o de classificaΓ§Γ£o. Os vΓdeos com as explicaΓ§Γ΅es teΓ³ricas estΓ£o disponΓveis no meu canal do YouTube
We pit Keras and PyTorch against each other, showing their strengths and weaknesses in action. We present a real problem, a matter of life-and-death: distinguishing Aliens from Predators!
Classify movie posters by genre
Implemented two papers for offline signature verification. Both use different deep learning techniques - Convolutional network and Siamese network.
Classifying 10 different categories of Sound using Deep Learning.
Source code for the paper "Reliable Deep Learning Plant Leaf Disease Classification Based on Light-Chroma Separated Branches".
Make a graph network of your followers. Based on username and gender
Source code for the paper "Color-aware two-branch DCNN for efficient plant disease classification".
Classification of Time-series data with RNN
QReLU and m-QReLU: Two novel quantum activation functions for Deep Learning in TensorFlow, Keras, and PyTorch
Multiple Handwritten Digit Recognition app Using Deep Learing - CNN from Canvas build on tkinter- GUI
AI Nexus π is a streamlined suite of AI-powered apps built with Streamlit. It features π StyleScan for fashion classification, π©Ί GlycoTrack for diabetes prediction, π’ DigitSense for digit recognition, πΈ IrisWise for iris species identification, π― ObjexVision for object recognition, and π GradeCast for GPA prediction with detailed insights.
RNN classifier built with Keras to classify MNIST dataset
Identify and classify objects in real-time video streams using TensorFlow and OpenCV. This project is designed for applications like security systems, robotics, and interactive installations, combining the power of TensorFlow for deep learning with OpenCV's webcam interaction.
Add a description, image, and links to the keras-classification-models topic page so that developers can more easily learn about it.
To associate your repository with the keras-classification-models topic, visit your repo's landing page and select "manage topics."