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22 search results found

🌲 Aimbot powered by real-time object detection with neural networks, GPU accelerated with Nvidia. Optimized for use with CS:GO.

Chromagan ⭐ 167

Official Implementation of ChromaGAN: An Adversarial Approach for Picture Colorization

Selfsupervised Denoising ⭐ 146

High-Quality Self-Supervised Deep Image Denoising - Official TensorFlow implementation of the NeurIPS 2019 paper

Frontalization ⭐ 139

Pytorch deep learning face frontalization model

Flownet2 Docker ⭐ 124

Dockerfile and runscripts for FlowNet 2.0 (estimation of optical flow)

Deep Homography Estimation Pytorch ⭐ 89

Deep homography network with Pytorch

Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

Dispnet Flownet Docker ⭐ 72

Dockerfile and runscripts for DispNet and FlowNet1 (estimation of disparity and optical flow)

The tutorials, datasets and source codes of the crosswalk detection (zebra crossing detection) network, which is robust in real scenes and real-time in Jetson nano. cross. detect. pedestrian.

Tlcbench ⭐ 51

Benchmark scripts for TVM

Nnvm Vision Demo ⭐ 49

Demos interesting image-in, image-out networks running on both NVIDIA and AMD GPUs, with NNVM

Whetstone ⭐ 32

Netdef Docker ⭐ 22

DispNet3, FlowNet3, FlowNetH, SceneFlowNet -- in Docker

Jetsonjs ⭐ 22

Embed a JavaScript/WebGL application on a Nvidia Jetson TX2 and stream the results through websockets. It does not rely on CUDA/Jetpack. HDMI touchscreen, virtual keyboard, GPIO control, wifi config are included.

An unofficial Torch implementation of InfoGAN

Chainer_imagenet_tools ⭐ 19

Nvidia_shield_remote ⭐ 18

Python class for controlling an Nvidia Shield over a network

Behavioralcloning For Self Driving Keras ⭐ 17

Keras/Tensorflow Implementation of `End-to-End Learning for self-driving car (Nvidia)`. Spatial factorization is additionally implemented to reduce network size.

frankenGAN networks

Humpback Whale Identification ⭐ 10

Kaggle Humpback whale identification: 2xGPU Data augmentation + FP16 mixed precision training

CARLsim is an efficient, easy-to-use, GPU-accelerated software framework for simulating large-scale spiking neural network (SNN) models with a high degree of biological detail.

A Deep Learning HTTP server based on nginx

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