Implementation of popular deep learning networks with TensorRT network definition API

This is a project focused on identifying the presence of pneumonia in chest X-ray images. Each image can be classified into one of three categories: Bacterial Pneumonia, Viral Pneumonia, or Normal.

A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

Knowledge Distillation for Skin Lesion Classification

The squeezenet image classification android example

Light-weight Single Person Pose Estimator

Snap List is an iOS app, which utilizes CoreML and SqueezeNet model to detect items using the user's camera and allows the user to add them to their list with persistence using UserDefaults.

How AI enhances parking management. In a team of four, we leveraged deep CNNs to create a system that detects (YOLO) and classifies (ensemble of SqueezeNet and ResNet) parking spots as free or occupied from images obtained by common surveillance cameras. It is able to employ pre-installed cameras, optimizing space utilization and reducing costs.

Implementation of SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size by Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, Kurt Keutzer.

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)

I am aiming to write different Semantic Segmentation models from scratch with different pretrained backbones.

deep learning models that detect the degrees of Alzheimer’s disease. applying various types of convolutional neural networks like SqueezeNet, DenseNet121 and VGG19 on ADNI dataset .

Architectures of convolutional neural networks for image classification in PyTorch

AoE (AI on Edge，终端智能，边缘计算) 是一个终端侧AI集成运行时环境 (IRE)，帮助开发者提升效率。

Real time camera object detection with Machine Learning in swift. Basic introduction to Core ML, Vision and ARKit.

Benchmarking various Computer Vision models on TinyImageNet Dataset

Attention Squeeze U-Net

SqueezeNet implementation with Keras Framework