CAR DAMAGE DETECTION
by SHIVAM GARG
CNN FOR DETECTING CAR DAMAGE
License: MIT License
Model stats and performance
Inference time in seconds per sample.
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
|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
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.
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