Facebook AI Performance Evaluation Platform

Classifies whether an image is of a dog or cat using pre-trained models

AI to detect and classify Pneumonia from Chest XRay Images

Building a powerful Neural network that can classify Natural Scenes around the world

Trained an image classifier to identify a total of 102 flower species. Data Augmentation was used to bring variety in the dataset. I also made a command-line interface for training and testing our model with various parameters using the ArgumentParser library in Python. Transfer Learning with VGG16 and Densenet121 was used to train our neural ne…

AI Programming with Python Nanodegree program Finle Capstone Project

Trabajo Fin de Máster: Estudio comparativo de un clasificador de imágenes en Raspberry Pi, de forma que se compara el tiempo de la inferencia en la Raspberry Pi con y sin el Neural Compute Stick (NCS). También se estudia como la complejidad de una red neuronal repercute en el tiempo de inferencia y se analiza si los tiempos obtenidos con el NCS …

This project uses pre-trained neuron network densenet121 and trains an image classifier to recognize 102 species of flowers with 89% accuracy.

Use Deep Learning model to diagnose 14 pathologies on Chest X-Ray and use GradCAM Model Interpretation Method

This repository exposes the final year computer engineering undergraduate project called xRayAID

A Deep Learning Projects For Diagnosis of COVID Disease by Chest X-Ray Images.

Followed a paper published on NIH. Replicated the same process and got similar results.

Classification and Gradient-based Localization of Chest Radiographs using PyTorch.

Ensemble based transfer learning approach for accurately classifying common thoracic diseases from Chest X-Rays

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An Image-Classifier made using pre-trained network/model 'densenet121' having an accuracy of 98%.

Facial Expression Recognition can be featured as one of the classification jobs people might like to include in the set of computer vision. The job of our project will be to look through a camera that will be used as eyes for the machine and classify the face of the person (if any) based on his current expression/mood.

This repository is used to create Machine Learning models. Building three kinds of models that include covid detection, fruit and vegetable nutrition content, and general disease detection.

Models Supported: DenseNet121, DenseNet161, DenseNet169, DenseNet201 and DenseNet264 (1D and 2D version with DEMO for Classification and Regression)