recognition-neural-network
There are 15 repositories under recognition-neural-network topic.
iamvukasin/flagnet
🏁 Flag recognition neural network + flag images dataset
pddpauw/BirdPi
A realtime acoustic bird classification system for the Raspberry Pi 5, based on BirdNET-Pi
BaseMax/ImageRecognition
Recognition of the images includes train and tests based on Python.
yaroslavbohonos/Handwritten-Digit-Recognition
The C++ neural network for handwritten digit recognition with online demo
ayalachn/HappyWhale
HappyWhale model implementation - Phase B
GTruf/Driver-Drowsiness-Detector
Prototype of an intelligent safety system for detecting driver drowsiness
abdulla20-8/handwritten_character_recogntion_application
An Android app to recognize YOUR hand-written Character
abdulla20-8/handwritten_number_recogntion_application
An Android app to recognize YOUR hand-writen digits
abdulla20-8/handwritten_realtime_character_recognition
A Real Time Kurdish handwriting Language Character Recognition Model Using Deep Learning (AlexNet) over used website
abdulla20-8/handwritten_realtime_number_recognition
A Real Time Kurdish handwriting Language Number Recognition Model Using Deep Learning (AlexNet) over used website
ADBeveridge/guise
Compare faces from images
ilmercu/ASL-recognition
American Sign Language (ASL) Recognition Project
loganbnelson/Logistic_MNIST_Digit_Recognition
This notebook uses the classic MNIST handwritten digit dataset, which contains digit 0-9 images of size 28*28. This can be used as self contained program for handwritten digit recognition of a file of data or as pieces to be implemented in other code. This is optimized for greyscale digit recognition.
Michprac/Actor_detection__YOLO_model
Diploma project, generating a YOLO model, recognizing people using YOLO, Maixduino microcontroller
RJGrove/NE-snake-prediction.github.io
As one can predict, many species look visually similar to the untrained eye, so developing software to help non-professionals can bring awareness to the various species. The development of deep learning networks is vital to the improvement of image segmentation when data is being drawn for quantitative purposes.