ID: 5dcea0ed503fb127005d370b

CAR DAMAGE DETECTION

by SHIVAM GARG

CNN FOR DETECTING CAR DAMAGE


License: MIT License

Tags: CNN Car Damage Detection Computer Vision Tensorflow Road Safety

 Model stats and performance
Framework Tensorflow
OS Used Linux
Inference time in seconds per sample.

Screenshots


CAR DAMAGE DETECTOR

WHAT IS IT?

MASKED Rcnn based car damage detector. Pipeline is as follows: A) Extracting Regions of Interest(ROI) An: By masked Rcnn B) Classification task: Regions are passed on to a fully connected network which classifies them into different image classes. In our case, it will be scratch(‘damage’) or background(car body without damage). C) Regression task: At last, a bounding box(BB) regression is used to predict the bounding boxes for each identified region for tightening the bounding boxes(getting exact BB defining relative coordinates)

How to use? To run the inference script run command RUN COMMANDS INSIDE CAR DETECTION FOLDER python run.py splash --weights mas k_rcnn_damage_0100.h5 --image 22.jpg

ARGUMENTS DETAILS HELP OPTIONS
splash MODE TRAIN OR DETECT
--weights MODELPATH Mention the model path
--image INPUT Mention the path Input

WHAT ARE THE REQUIREMENTS?

To get all the requirements and dependencies installed run the command For GPU - pip install -r gpu_requirements.txt For CPU - pip install -r cpu_requirements.txt

Author View Profile

SHIVAM GARG
New Delhi, India
Pro
41
LEVEL

5464 Profile
Views

A philosophy student cleverly disguised as a Coax Deep Learning engineer spending whole day, practically every day, experimenting with TensorFlow,Pytorch, and Caffe; dabbling with Python and C++; and drinking a wide variety of Coffee everyday.

User Reviews



0 total ratings

Model has not been reviewed yet.

More by this user | Show All



Also checkout...