#算法刷题#A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities
翻译 - python库旨在使开发人员能够使用独立的计算机视觉功能来构建应用程序和系统
#计算机科学#Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, N...
翻译 - 在cifar100上进行实践（ResNet，DenseNet，VGG，GoogleNet，InceptionV3，InceptionV4，Inception-ResNetv2，Xception，Resnet In Resnet，ResNext，ShuffleNet，ShuffleNetv2，MobileNet，MobileNetv2，SqueezeNet，NasNet，残留注意力网络，SENet）
Classification models trained on ImageNet. Keras.
#计算机科学#InceptionTime: Finding AlexNet for Time Series Classification
#计算机科学#food image to recipe with deep convolutional neural networks.
Sign Language Gesture Recognition From Video Sequences Using RNN And CNN
#计算机科学#Mobile AI Compute Engine Model Zoo
#IOS#Core ML demo app with Unsplash API
#计算机科学#A neural network to generate captions for an image using CNN and RNN with BEAM Search.
#计算机科学#Simple sign language alphabet recognizer using Python, openCV and tensorflow for training Inception model (CNN classifier).
#计算机科学#A Multiclass Weed Species Image Dataset for Deep Learning
Detecting Pneumonia in Chest X-ray Images using Convolutional Neural Network and Pretrained Models
#计算机科学#COVID-19 Detection Chest X-rays and CT scans: COVID-19 Detection based on Chest X-rays and CT Scans using four Transfer Learning algorithms: VGG16, ResNet50, InceptionV3, Xception. The models were tra...
#计算机科学#Deploying Keras models using TensorFlow Serving and Flask
BEST SCORE ON KAGGLE SO FAR , EVEN BETTER THAN THE KAGGLE TEAM MEMBER WHO DID BEST SO FAR. The project is about diagnosing pneumonia from XRay images of lungs of a person using self laid convolutional...
This is the code repository for my Medium post "Understanding your Convolution network with Visualizations"
Supervised Classification of bird species 🐦 in high resolution images, especially for, Himalayan birds, having diverse species with fairly low amount of labelled data [ICVGIPW'18]
Transfer Learning with DCNNs (DenseNet, Inception V3, Inception-ResNet V2, VGG16) for skin lesions classification