Summary & Implementation of Deep Learning research paper in Tensorflow/Pytorch.

Models Supported: Inception [v1, v2, v3, v4], SE-Inception, Inception_ResNet [v1, v2], SE-Inception_ResNet (1D and 2D version with DEMO for Classification and Regression)

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

My PyTorch implementation of CNNs. All networks in this repository are using CIFAR-100 dataset for training.

TensorFlow Lite classification on a bare Raspberry Pi 4 with 64-bit OS at 23 FPS

TensorFlow implementation of GoogLeNet.

This repository is the implementation of several famous convolution neural network architecture with Keras. (Resnet v1, Resnet v2, Inception v1/GoogLeNet, Inception v2, Inception v3))

Computer Vision .Libraries used matplot, numpy, openCV, mazelib standard machine learning libs you know dude

使用TensorFlow自己搭建一些经典的CNN模型，并使用统一的数据来测试效果。

Cow Detection

PyTorch implements `Rethinking the Inception Architecture for Computer Vision` paper.

implementation of Inflated 3D ConvNet in TensorFlow

TensorFlow Lite classification on a bare Raspberry Pi 4 at 33 FPS

TensorFlow based mobile neural network model resources

Realtime Face Recognition using FaceNet architecture

Implementation of various state-of-the-art architectures in Tensorflow, Keras and Python

💵Model Peruvian Bills (MLR, Mask, Inceptionv2) RCNN💶

Create your own databse, compile tripletloss with pre-trained FaceNet model, run real-time face recognition on local host

This project was completed under the course Deep Learning(CSE674) at University at Buffalo.

The project focuses on classifying brain tumors using the Multi-Modal Squeeze and Excitation Network.