Simple Example of Image Recognition
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Updated
Jun 27, 2019 - Python
Simple Example of Image Recognition
Using transfer learning to predict if there exists a cotton disease in the plant or not. The best were the inceptionv3 model and the ResNet50 model and then finally made a model for the web using flask for an end-to-end deployment of this project.
A Artifitial Intelligent Securtiy system that detects humans using a camera and beeps an alarm .
Here is an implementation of InceptionV3 and VGG-16 models in Python from scratch. These models were then trained on a dataset of handwritten alphabets. An experiment was carried out to achieve higher accuracy by using different combinations of optimizers and learning rates. These models were then compared to the inbuilt models in Python.
Smart bike using deep learning and iot
an implementation of the Convolutional Neural Network model and Transfer Learning (InceptionV3) model to classify horse or human images.
Food Crop disease detection model using InceptionV3 architecture
Using CNN to classify dog's breed. Inception v3 model has been trained using Transfer Learning.
Image recognition on CIFAR 10, CIFAR 100, Caltech 101 and Caltech 256 datasets. With the implementation of WideResNet, InceptionV3 and DenseNet neural networks.
Implementation of a deep learning model for leukemia classification and malaria detection using CNNs. The model utilizes transfer learning with VGG19 and InceptionV3 architectures, trained on custom datasets. The code includes data preprocessing, model training, evaluation, and visualization of performance metrics. Achieved high accuracy
Automated Web-based Malaria Detection System Using Machine Learning, Deep Learning and Transfer Learning Techniques: A Comparative Analaysis
Used CNN architecture and pre trained weights of VGG16 to detect brain tumor from Images.
A Comparative Study of the performance of CNN from scratch compared to a transfer learning approach (InceptionV3)
This repository contains the code and resources for a deep learning project aimed at recognizing hand signs for the game of Rock-Paper-Scissors. The project utilizes convolutional neural networks (CNNs) to classify hand signs captured through a webcam, enabling users to play the game without the need for physical gestures.
Various codes and scripts used during AI research, all neatly organised
Jupyter notebook was made for doing machine learning which classify images
Identifying vehicle and appliance damage from an image on a scale of low, moderate, high
Its a convNet built upon InceptionV3 and trained on 928 pokemon classes.
Udacity's Deep Learning Nanodegree Project - Dog-Breed Classifier
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