size_categories:1K<n<10K language:en climate region:us
\
AI4Good project for detecting waste in environment. www.detectwaste.ml.

Our latest results were published in Waste Management journal in article titled Deep learning-based waste detection in natural and urban environments.

You can find more technical details in our technical report Waste detection in Pomerania: non-profit project for detecting waste in environment.

Did you know that we produce 300 million tons of plastic every year? And only the part of it is properly recycled.

The idea of detect waste project is to use Artificial Intelligence to detect plastic waste in the environment. Our solution is applicable for video and photography. Our goal is to use AI for Good.
\
@article{MAJCHROWSKA2022274,
title = {Deep learning-based waste detection in natural and urban environments},
journal = {Waste Management},
volume = {138},
pages = {274-284},
year = {2022},
issn = {0956-053X},
doi = {https://doi.org/10.1016/j.wasman.2021.12.001},
url = {https://www.sciencedirect.com/science/article/pii/S0956053X21006474},
author = {Sylwia Majchrowska and Agnieszka Mikołajczyk and Maria Ferlin and Zuzanna Klawikowska and Marta A. Plantykow and Arkadiusz Kwasigroch and Karol Majek},
keywords = {Object detection, Semi-supervised learning, Waste classification benchmarks, Waste detection benchmarks, Waste localization, Waste recognition},
abstract = {Waste pollution is one of the most significant environmental issues in the modern world. The importance of recycling is well known, both for economic and ecological reasons, and the industry demands high efficiency. Current studies towards automatic waste detection are hardly comparable due to the lack of benchmarks and widely accepted standards regarding the used metrics and data. Those problems are addressed in this article by providing a critical analysis of over ten existing waste datasets and a brief but constructive review of the existing Deep Learning-based waste detection approaches. This article collects and summarizes previous studies and provides the results of authors’ experiments on the presented datasets, all intended to create a first replicable baseline for litter detection. Moreover, new benchmark datasets detect-waste and classify-waste are proposed that are merged collections from the above-mentioned open-source datasets with unified annotations covering all possible waste categories: bio, glass, metal and plastic, non-recyclable, other, paper, and unknown. Finally, a two-stage detector for litter localization and classification is presented. EfficientDet-D2 is used to localize litter, and EfficientNet-B2 to classify the detected waste into seven categories. The classifier is trained in a semi-supervised fashion making the use of unlabeled images. The proposed approach achieves up to 70% of average precision in waste detection and around 75% of classification accuracy on the test dataset. The code and annotations used in the studies are publicly available online11https://github.com/wimlds-trojmiasto/detect-waste..}
}
---
dataset_info:
  config_name: taco-multi
  features:
  - name: image_id
    dtype: int64
  - name: image
    dtype: image
  - name: width
    dtype: int32
  - name: height
    dtype: int32
  - name: objects
    sequence:
    - name: id
      dtype: int64
    - name: area
      dtype: int64
    - name: bbox
      sequence: float32
      length: 4
    - name: category
      dtype:
        class_label:
          names:
            '0': metals_and_plastic
            '1': other
            '2': non_recyclable
            '3': glass
            '4': paper
            '5': bio
            '6': unknown
  splits:
  - name: train
    num_bytes: 1006510
    num_examples: 3647
  - name: test
    num_bytes: 248312
    num_examples: 915
  download_size: 10265127938
  dataset_size: 1254822
language:
- en
tags:
- climate
pretty_name: detect-waste
size_categories:
- 1K<n<10K
---

# Dataset Card for detect-waste

## Dataset Description

- **Homepage: https://github.com/wimlds-trojmiasto/detect-waste** 

### Dataset Summary

AI4Good project for detecting waste in environment. www.detectwaste.ml.

Our latest results were published in Waste Management journal in article titled Deep learning-based waste detection in natural and urban environments.

You can find more technical details in our technical report Waste detection in Pomerania: non-profit project for detecting waste in environment.

Did you know that we produce 300 million tons of plastic every year? And only the part of it is properly recycled.

The idea of detect waste project is to use Artificial Intelligence to detect plastic waste in the environment. Our solution is applicable for video and photography. Our goal is to use AI for Good.


### Supported Tasks and Leaderboards

Object Detection

### Languages

English

### Data Fields

https://github.com/wimlds-trojmiasto/detect-waste/tree/main/annotations

## Dataset Creation

The images are post processed to remove exif and reorient as required. Some images are labelled without the exif rotation in mind thus they're not rotated at all but have their exif metadata removed

### Personal and Sensitive Information

**BEWARE** This repository had been created by a third-party and is not affiliated in any way with the original detect-waste creators/

## Considerations for Using the Data

### Licensing Information

https://raw.githubusercontent.com/wimlds-trojmiasto/detect-waste/main/LICENSE