simple transfer learning example form Inceptoinv3
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Updated
Jul 23, 2017 - Python
simple transfer learning example form Inceptoinv3
Example models for cifar10 classification
Scraping prices from web and clustering images to fit price categories.
This complete project is made as a part of Data Science Internship at iNeuron.ai Refer to the README for more detailed explanation about the project!
A ML model which helps the differently abled people(i.e, deaf , dumb & blind) to communicate
Accurate image classification powered by InceptionV3 deep learning model. Quickly classify diverse images with high precision using TensorFlow.
Blindness Detection - Machine Learning Model for Diabetic Retinopathy Patients
Demo of Inception v3.
Simple Example of Image Recognition
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
A deep learning model that generates captions for camera trap images in the Snapshot Serengeti dataset.
an implementation of the Convolutional Neural Network model and Transfer Learning (InceptionV3) model to classify horse or human images.
Multi image label classification by multi models.
Using CNN to classify dog's breed. Inception v3 model has been trained using Transfer Learning.
pytorch implementations of some DL architectures. Includes googlenet, resnet, inceptionV3, densenet, mobilenetV1, mobilenetV2, senet, efficientnetV1, transformer etc.
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.
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