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

Last update: Jun 22, 2022

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

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

Related tags

Overview

Object_detection

To run:

List of classes to choose from:

Owner

Mateusz Kurdziel

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

Related tags

Overview

Object_detection

To run:

List of classes to choose from:

Owner

Mateusz Kurdziel

Wide Residual Networks (WideResNets) in PyTorch

Implementation of Google Brain's WaveGrad high-fidelity vocoder

Using LSTM to detect spoofing attacks in an Air-Ground network

Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning

This is the source code of the solver used to compete in the International Timetabling Competition 2019.

Official implementation of the RAVE model: a Realtime Audio Variational autoEncoder

Open source person re-identification library in python

Consensus score for tripadvisor

PyTorch(Geometric) implementation of G^2GNN in "Imbalanced Graph Classification via Graph-of-Graph Neural Networks"

League of Legends Reinforcement Learning Environment (LoLRLE) multiple training scenarios using PPO.

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing.

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RIFE - Real-Time Intermediate Flow Estimation for Video Frame Interpolation

Few-NERD: Not Only a Few-shot NER Dataset

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