Lukas Cavigelli's repositories
ExtendedBitPlaneCompression
Provides the code for the paper "EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training Accelerators" by Lukas Cavigelli, Georg Rutishauser, Luca Benini.
FANN-on-ARM
FANN-on-ARM: Optimized FANN Inference for ARM Cortex M-series
CUDA-Winograd
Fast CUDA Kernels for ResNet Inference. Using Winograd algorithm to optimize the efficiency of convolutional computing.
human-pose-estimation.pytorch
The project is an official implement of our ECCV2018 paper "Simple Baselines for Human Pose Estimation and Tracking(https://arxiv.org/abs/1804.06208)"
pytorch-revnet
Implementation of the reversible residual network in pytorch
pytorch-ssd
MobileNetV1, MobileNetV2, VGG based SSD/SSD-lite implementation in Pytorch. Out-of-box support for retraining on Open Images dataset. ONNX and Caffe2 support. Experiment Ideas like CoordConv.
pytorch-yolo-v3
A PyTorch implementation of the YOLO v3 object detection algorithm
pytorch-yolo2
Convert https://pjreddie.com/darknet/yolo/ into pytorch
PyTorch-YOLOv3
Minimal PyTorch implementation of YOLOv3
SDE-Interview-Questions
Most comprehensive list :clipboard: of tech interview questions :blue_book: of companies scraped from Geeksforgeeks, CareerCup and Glassdoor.
ssd.pytorch
A PyTorch Implementation of Single Shot MultiBox Detector
XNOR-Net-PyTorch
PyTorch Implementation of XNOR-Net
BBox-Label-Tool
A simple tool for labeling object bounding boxes in images
combinational-bnn
System Verilog code describing a fully combinational binarized neural network.
driving-object-detection
Traffic Light detection using Tensorflow Object Detection API v1 and v2
FlexFlow
A distributed deep learning framework.
HashingDeepLearning
Codebase for "SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems"
SSD-Tensorflow
Single Shot MultiBox Detector in TensorFlow
stream-ebpc
Provides the hardware code for the paper "EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training Accelerators" by Lukas Cavigelli, Georg Rutishauser, Luca Benini.