Crowd Counting using Xception

Image caption generator using VGG16 and XCeption for feature extraction and LSTM for generating captions from the Flickr8k dataset

Fire Detection on images using Xception and dense CNNs: This project uses convolutional neural networks (CNNs) to detect fire in images, comparing the performance of three different models and visualizing predictions from the fittest.

Workshop CDK Template to provision infra for the Deep Visual Search workshop

Stanford dogs dataset breed classification with Xception (CNN)

Search for similar images in an arbitrary dataset

Advanced COVID-19 Detection From Lung X-Rays With Deep Learning

Automated Web-based Malaria Detection System Using Machine Learning, Deep Learning and Transfer Learning Techniques: A Comparative Analaysis

Classifying images of rice leaf disease plants into various disease categories using Convolutional Neural Network

Real-time Facial Emotion Recognition

DeepLab v3+ model in PyTorch. Support different backbones.

Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)

Support PointRend, Fast_SCNN, HRNet, Deeplabv3_plus(xception, resnet, mobilenet), ContextNet, FPENet, DABNet, EdaNet, ENet, Espnetv2, RefineNet, UNet, DANet, HRNet, DFANet, HardNet, LedNet, OCNet, EncNet, DuNet, CGNet, CCNet, BiSeNet, PSPNet, ICNet, FCN, deeplab)

Classification of flowers using Convolutional Neural Network

This project utilizes VGG19, Xception, and a custom CNN to classify retinal diseases from OCT images. The custom CNN achieved 95.47% accuracy, demonstrating AI's potential in improving diagnostic accuracy for ophthalmic disorders. Additionally, a Flask-based web app enables users to upload images for real-time predictions.

A react application with a deep learning model to generate caption for images

This repository accompanies our research paper and includes all the essential files that support our findings on fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification

🎉A comprehensive project for skin cancer detection using a CNN model.

This repository contains code for comparing and evaluating various CNN classification models on a waste image dataset.

Xception implemented with caffe