American sign langauge detection using cnn
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Updated
Jul 10, 2023 - Jupyter Notebook
American sign langauge detection using cnn
Data augmentation is a technique of artificially increasing the training set by creating modified copies of a dataset using existing data. Here is a notebook of different augmentation techniques.
🌾🍀 An early crop disease discovery Deep Convolutional Neural Networks model using leaf symptom images.
Solid Waste Detection with Convolutional Neural Networks (CNN)
For correctly predicting the crops from the images provided
The augmented image processing for a cgi generated dataset of humans and horses using to train a CNN model.
Train Your CNN model on any object without any need of your custom dataset by using webscraping
Just a practice to implement CNN on one of my favouraite games
Classification using advanced Convolution Neural Networks and the Intel Image dataset, featuring 6 classes of color pictures in 150x150 pixels resolution.
Using a Convolutional Neural Network to determine the label of a photo. Picture scraping via an API.
It Contains a Model which Recognizes Handwritten letters using CNN (Convolutional Neural Network).
predicting image category using convolutional neural networks
Jupyter notebooks creating Keras models to classify dog & cat breeds based on limited dataset, with and without transfer learning
Computer Vision using CNN on dataset consisting of real world images of humans & horses.
Ultrasound Image Recognition
A CCN model for identifying pneumonia from x-rays with 91.67% accuracy.
An innovative Traffic Sign Classification project using Convolutional Neural Networks (CNNs) to enhance road safety and traffic management.
A comparison between Transfer Learning and custom Convolutional Network to classify images.
This repository presents an approach as part of my final year project for automatic traffic sign recognition using Convolutional Neural Network
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