vck/yolov3-pytorch-garbage-detection

Test and prediction code for a garbage object detection

Garbage detection using PyTorch and YoloV3

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.

This repository combines elements from:

• https://github.com/zhaoyanglijoey/yolov3
• https://github.com/ultralytics/yolov3

Test and prediction code for a garbage object detection

Installation

To install all required libaries:

pip install -r requirements.txt

Predictions

Several different weights and configs are available at: https://drive.google.com/open?id=1DjeNxdaF7AW3Nu54_3oRw_1SeYJtOvNL. Some also have the testing data.

Pre trained weights

Name	Classes	Test data
3 classes	cardboard, garbage_bags and containers	Yes
cigarettes	cigarette	Yes
9 classes	container_small, garbage_bag, cardboard, matras, christmas_tree, graffiti, pole, face_privacy_filter and license_plate_privacy_filter	No

Run 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/drive/folders/1DjeNxdaF7AW3Nu54_3oRw_1SeYJtOvNL

The garbage bags, containers and cardboard dataset contains 804 images and label files. A smaller dataset with annotations of cigarettes is also available.

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

Training

For training a new model look at:

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

This is the training loss of 1400 images with 9 classes: