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c:\Users\foleyc\code\python\yolov3-pytorch-garbage-detection>python detector_garb.py -i rtsp://10.0.172.54/stream1 --video -o ./output/

Demo 2

Garbage detection using PyTorch and YoloV3

(Work in progress)

For more information, look at this medium post.

PyTorch implementation of a garbage detection model. This repository contains all code for predicting/detecting and evaulating the model. The current version can detect garbage bags, cardboard and household waste containers.

This repository combines elements from:

Demo 1

Test and prediction code for a garbage object detection

Installation

To install all required libaries:

pip install -r requirements.txt

Predictions

To run predictions, download the cfg and weights from https://drive.google.com/open?id=1DjeNxdaF7AW3Nu54_3oRw_1SeYJtOvNL and put them in the correct folders.

Then for example run the following the make a prediction on a file using CPU:

python detector_garb.py -i samples/input5_frame11.jpg -o output

Or to realtime detect on your webcam using GPU: (CUDA must be installed)

python detector_garb.py -i 0 --webcam --video -o ./webcam_output/ --cuda

Docker

To run code in docker

docker-compose build
docker-compose up

Test

For testing download data from: https://drive.google.com/open?id=1DjeNxdaF7AW3Nu54_3oRw_1SeYJtOvNL

The dataset contains 804 images and label files.

To run test execute the following code:

python test.py
Class Images Targets P R mAP F1
all 115 579 0.242 0.941 0.875 0.376
container_small 115 180 0.38 0.989 0.979 0.549
garbage_bag 115 223 0.212 0.964 0.875 0.348
cardboard 115 176 0.122 0.869 0.77 0.231

test_example

Training

For training a new model look at:

https://github.com/maartensukel/yolov3-garbage-object-detection-training

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