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)

Classification models trained on ImageNet. Keras.

A tensorflow2 implementation of some basic CNNs(MobileNetV1/V2/V3, EfficientNet, ResNeXt, InceptionV4, InceptionResNetV1/V2, SENet, SqueezeNet, DenseNet, ShuffleNetV2, ResNet).

AI场景分类竞赛

CBAM implementation on TensorFlow Slim

CBAM implementation on TensowFlow

SENet implementation on TensorFlow Slim

Supervised Classification of bird species 🐦 in high resolution images, especially for, Himalayan birds, having diverse species with fairly low amount of labelled data

A PyTorch implementation of Inception-v4 and Inception-ResNet-v2.

Tensorflow Implementation of FaceNet: A Unified Embedding for Face Recognition and Clustering to find the celebrity whose face matches the closest to yours.

DeepFake Detection: Detect the video is fake or not using InceptionResNetV2.

Keras Implementation of major CNN architectures

inception in tensorflow

A tensorflow2 implementation of Inception_V4, Inception_ResNet_V1 and Inception_ResNet_V2.

Regression in Convolutional Neural Network applied to Plant Leaf Count

Tensorflow implementation of music-genres-classification with InceptionResnetV2

Content-based image retrieval example using Deep Neural Networks (Inception Res-Net V2) in Keras

MATLAB Fashion Classification with Deep Learning.

Deep Learning in Medicine Final Project

Landmark Recognition(Google Challenge on Kaggle) Using Transfer Learning

A set of notebooks that leverage classical ML algorithms and DL neural nets using TF, Keras and Theano to address a series of issues in the field of conservation and biology.

Solutions to the Advanced CNN course by the Lazy Programmer and all CNN Models I've worked on

The dataset, provides 18000 images of 50 animal classes with pre-extracted feature representation for each image. The challenge was to build prediction models that can identify all the attributes associated with each animal.

A tool for training and using an Inception-Resnet-V2 convolutional network for image classification, using transfer learning.

Diabetic Retinopathy Prediction - Deep Learning Models

Classify images of food into different classes using Deep Learning.

Malaria is one of the disease causing deaths worldwide. Detection of malaria is a physical task invloving pathologist to diagnosis the presence of parasite inside the microscopic view of thin blood smears. This process is prone to errors as it requires an experienced person. Also, eye sight plays a vital role in order to detect the malaria. Therefore, in this repository I have tried to create a solution for the malaria detection by ensembling (bundling) pretrained models such as InceptionResNetV2, denseNet and VGG19 followed by max voting.

Image Caption Generation using Keras' Pre-Trained Image Feature Extraction models and LSTM

real time text detection with deep learning