App for identification of various objects. Based on YOLO v4 tiny architecture

Overview

Object_detection

Repository containing trained model yolo v4 tiny, which is capable of identification 80 different classes

  • Default feed is set to be a computer camera
  • GUI contains of 5 classes which user can choose when initialising a function

To run:

  • select the 'detect' function arguments, all five should be indicated

List of classes to choose from:

  • person
  • bicycle
  • car
  • motorbike
  • aeroplane
  • bus
  • train
  • truck
  • boat
  • traffic light
  • fire hydrant
  • stop sign
  • parking meter
  • bench
  • bird
  • cat
  • dog
  • horse
  • sheep
  • cow
  • elephant
  • bear
  • zebra
  • giraffe
  • backpack
  • umbrella
  • handbag
  • tie
  • suitcase
  • frisbee
  • skis
  • snowboard
  • sports ball
  • kite
  • baseball bat
  • baseball glove
  • skateboard
  • surfboard
  • tennis racket
  • bottle
  • wine glass
  • cup
  • fork
  • knife
  • spoon
  • bowl
  • banana
  • apple
  • sandwich
  • orange
  • broccoli
  • carrot
  • hot dog
  • pizza
  • donut
  • cake
  • chair
  • sofa
  • pottedplant
  • bed
  • diningtable
  • toilet
  • tvmonitor
  • laptop
  • mouse
  • remote
  • keyboard
  • cell phone
  • microwave
  • oven
  • toaster
  • sink
  • refrigerator
  • book
  • clock
  • vase
  • scissors
  • teddy bear
  • hair drier
  • toothbrush
Owner
Mateusz Kurdziel
Mateusz Kurdziel
A PyTorch implementation of SlowFast based on ICCV 2019 paper "SlowFast Networks for Video Recognition"

SlowFast A PyTorch implementation of SlowFast based on ICCV 2019 paper SlowFast Networks for Video Recognition. Requirements Anaconda PyTorch conda in

Hao Ren 8 Dec 23, 2022
ContourletNet: A Generalized Rain Removal Architecture Using Multi-Direction Hierarchical Representation

ContourletNet: A Generalized Rain Removal Architecture Using Multi-Direction Hierarchical Representation (Accepted by BMVC'21) Abstract: Images acquir

10 Dec 08, 2022
A small library for creating and manipulating custom JAX Pytree classes

Treeo A small library for creating and manipulating custom JAX Pytree classes Light-weight: has no dependencies other than jax. Compatible: Treeo Tree

Cristian Garcia 58 Nov 23, 2022
Pytorch implementation of RED-SDS (NeurIPS 2021).

Recurrent Explicit Duration Switching Dynamical Systems (RED-SDS) This repository contains a reference implementation of RED-SDS, a non-linear state s

Abdul Fatir 10 Dec 02, 2022
BLEND: A Fast, Memory-Efficient, and Accurate Mechanism to Find Fuzzy Seed Matches

BLEND is a mechanism that can efficiently find fuzzy seed matches between sequences to significantly improve the performance and accuracy while reducing the memory space usage of two important applic

SAFARI Research Group at ETH Zurich and Carnegie Mellon University 19 Dec 26, 2022
A PyTorch implementation: "LASAFT-Net-v2: Listen, Attend and Separate by Attentively aggregating Frequency Transformation"

LASAFT-Net-v2 Listen, Attend and Separate by Attentively aggregating Frequency Transformation Woosung Choi, Yeong-Seok Jeong, Jinsung Kim, Jaehwa Chun

Woosung Choi 29 Jun 04, 2022
U-Time: A Fully Convolutional Network for Time Series Segmentation

U-Time & U-Sleep Official implementation of The U-Time [1] model for general-purpose time-series segmentation. The U-Sleep [2] model for resilient hig

Mathias Perslev 176 Dec 19, 2022
Multi-Anchor Active Domain Adaptation for Semantic Segmentation (ICCV 2021 Oral)

Multi-Anchor Active Domain Adaptation for Semantic Segmentation Munan Ning*, Donghuan Lu*, Dong Wei†, Cheng Bian, Chenglang Yuan, Shuang Yu, Kai Ma, Y

Munan Ning 36 Dec 07, 2022
Baseline model for "GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping" (CVPR 2020)

GraspNet Baseline Baseline model for "GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping" (CVPR 2020). [paper] [dataset] [API] [do

GraspNet 209 Dec 29, 2022
Controlling a game using mediapipe hand tracking

These scripts use the Google mediapipe hand tracking solution in combination with a webcam in order to send game instructions to a racing game. It features 2 methods of control

3 May 17, 2022
SCAAML is a deep learning framwork dedicated to side-channel attacks run on top of TensorFlow 2.x.

SCAAML (Side Channel Attacks Assisted with Machine Learning) is a deep learning framwork dedicated to side-channel attacks. It is written in python and run on top of TensorFlow 2.x.

Google 69 Dec 21, 2022
PyTorch version of the paper 'Enhanced Deep Residual Networks for Single Image Super-Resolution' (CVPRW 2017)

About PyTorch 1.2.0 Now the master branch supports PyTorch 1.2.0 by default. Due to the serious version problem (especially torch.utils.data.dataloade

Sanghyun Son 2.1k Dec 27, 2022
Audio-Visual Generalized Few-Shot Learning with Prototype-Based Co-Adaptation

Audio-Visual Generalized Few-Shot Learning with Prototype-Based Co-Adaptation The code repository for "Audio-Visual Generalized Few-Shot Learning with

Kaiaicy 3 Jun 27, 2022
A Quick and Dirty Progressive Neural Network written in TensorFlow.

prog_nn .▄▄ · ▄· ▄▌ ▐ ▄ ▄▄▄· ▐ ▄ ▐█ ▀. ▐█▪██▌•█▌▐█▐█ ▄█▪ •█▌▐█ ▄▀▀▀█▄▐█▌▐█▪▐█▐▐▌ ██▀

SynPon 53 Dec 12, 2022
YolactEdge: Real-time Instance Segmentation on the Edge

YolactEdge, the first competitive instance segmentation approach that runs on small edge devices at real-time speeds. Specifically, YolactEdge runs at up to 30.8 FPS on a Jetson AGX Xavier (and 172.7

Haotian Liu 1.1k Jan 06, 2023
PyTorch implementation of our ICCV 2021 paper, Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agents.

PyTorch implementation of our ICCV 2021 paper, Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agents.

Saim Wani 4 May 08, 2022
Sample Code for "Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL"

Sample Code for "Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL" This is the official codebase for Pessimism Meets I

3 Sep 19, 2022
Direct design of biquad filter cascades with deep learning by sampling random polynomials.

IIRNet Direct design of biquad filter cascades with deep learning by sampling random polynomials. Usage git clone https://github.com/csteinmetz1/IIRNe

Christian J. Steinmetz 55 Nov 02, 2022
HyperaPy: An automatic hyperparameter optimization framework ⚡🚀

hyperpy HyperPy: An automatic hyperparameter optimization framework Description HyperPy: Library for automatic hyperparameter optimization. Build on t

Sergio Mora 7 Sep 06, 2022
Defending graph neural networks against adversarial attacks (NeurIPS 2020)

GNNGuard: Defending Graph Neural Networks against Adversarial Attacks Authors: Xiang Zhang ( Zitnik Lab @ Harvard 44 Dec 07, 2022