TACO: Trash Annotations in Context Dataset

From: Pedro F. Proença; Pedro Simões

• For more information, go to: http://tacodataset.org
• https://github.com/pedropro/TACO https://raw.githubusercontent.com/wiki/pedropro/TACO/images/logonav.png" alt="TACO Logo">

TACO is a growing image dataset of trash in the wild. It contains segmented images of litter taken under diverse environments: woods, roads and beaches. These images are manually labeled according to an hierarchical taxonomy to train and evaluate object detection algorithms. Annotations are provided in a similar format to COCO dataset.

The model in action:

https://raw.githubusercontent.com/wiki/pedropro/TACO/images/teaser.gif" alt="Gif of the model running inference">

Examples images from the dataset:

https://raw.githubusercontent.com/wiki/pedropro/TACO/images/2.png" alt="Example Image #2 from the Dataset"> https://raw.githubusercontent.com/wiki/pedropro/TACO/images/5.png" alt="Example Image #5 from the Dataset">

For more details and to cite the authors:

• Paper: https://arxiv.org/abs/2003.06975
• Paper Citation: @article{taco2020, title={TACO: Trash Annotations in Context for Litter Detection}, author={Pedro F Proença and Pedro Simões}, journal={arXiv preprint arXiv:2003.06975}, year=

The TACO: Trash Annotations in Context is an open image dataset that focuses on waste in various real-world settings. It encompasses a collection of images depicting litter in diverse environments, ranging from tropical beaches to urban streets in places like London. The dataset is notable for its manual labeling and segmentation, providing a hierarchical taxonomy for object detection algorithms to train and evaluate their performance. It comprises 1,500 images that cover 60 distinct waste classes, including items like aluminum_foil, batterie, and aluminum_blister_pack.

Yolov5 Garbage Detection

## Overview

 Yolov5 Garbage Detection is a dataset for object detection tasks - it contains Garbage annotations for 5,980 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The TACO (Trash Annotations in Context) dataset, now made available in YOLO (You Only Look Once) format on Kaggle, is a comprehensive dataset that is designed for the detection and classification of litter (trash). Originally provided by 'Papers with Code', this version has been processed for direct usage in YOLO-based object detection models.

TACO comprises a diverse range of high-resolution images of various types of litter in different contexts and environments. The dataset encompasses a broad variety of litter categories that are commonly found in our surroundings, making it a valuable asset for training models for environmental cleanup and monitoring purposes.

Each image in this dataset is associated with a respective annotation file (.txt file), as per the YOLO dataset standard. These annotation files contain the coordinates of bounding boxes for the litter present in the image and the respective classes of this litter. The bounding box annotations are normalized according to the image size, ranging from 0 to 1.

The primary goal of this dataset is to support the development of robust and accurate object detection models for litter identification and classification. This can help create effective solutions for environmental problems such as pollution and littering, and potentially contribute to the development of automated cleanup systems.

Although the dataset isn't split into separate training, validation, or testing subsets, users are encouraged to make such divisions as per their model development requirements.

Please abide by the terms and conditions specified by the original dataset providers when using this dataset. If you find this dataset beneficial for your research or project, do consider citing the original source to acknowledge the creators' efforts.

Dataset: COCO-Formatted Object Detection Dataset

Overview

This dataset is designed for object detection tasks and follows the COCO format. It contains 300 images and corresponding annotation files in JSON format. The dataset is split into training, validation, and test sets, ensuring a balanced distribution for model evaluation.

Dataset Structure

The dataset is organized into three main folders:

train/ (70% - 210 images)

valid/ (15% - 45 images)

test/ (15% - 45 images)

Each folder contains:

Images in JPEG/PNG format.

A corresponding _annotations.coco.json file that includes bounding box annotations.

Preprocessing & Augmentations

The dataset has undergone several preprocessing and augmentation steps to enhance model generalization:

Image Preprocessing:

Auto-orientation applied

Resized to 640x640 pixels (stretched)

Augmentation Techniques:

Flip: Horizontal flipping

Crop: 0% minimum zoom, 5% maximum zoom

Rotation: Between -5° and +5°

Saturation: Adjusted between -4% and +4%

Brightness: Adjusted between -10% and +10%

Blur: Up to 0px

Noise: Up to 0.1% of pixels

Bounding Box Augmentations:

Flipping, cropping, rotation, brightness adjustments, blur, and noise applied accordingly to maintain annotation consistency.

Annotation Format

The dataset follows the COCO (Common Objects in Context) format, which includes:

images section: Contains image metadata such as filename, width, and height.

annotations section: Includes bounding boxes, category IDs, and segmentation masks (if applicable).

categories section: Defines class labels.

Yolo V8 Trash Detection EE4016

## Overview

Yolo V8 Trash Detection EE4016 is a dataset for object detection tasks - it contains Trash annotations for 2,527 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The TrashCan dataset is an instance-segmentation dataset of underwater trash. It is comprised of annotated images (7,212 images) which contain observations of trash, ROVs, and a wide variety of undersea flora and fauna. The annotations in this dataset take the format of instance segmentation annotations: bitmaps containing a mask marking which pixels in the image contain each object. The imagery in TrashCan is sourced from the J-EDI (JAMSTEC E-Library of Deep-sea Images) dataset, curated by the Japan Agency of Marine Earth Science and Technology (JAMSTEC).

COCO Trash

## Overview

COCO Trash is a dataset for object detection tasks - it contains Trash annotations for 3,738 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset is collected by Datacluster Labs. To download full dataset or to submit a request for your new data collection needs, please drop a mail to: sales@datacluster.ai This dataset is an extremely challenging set of over 9000+ original Trash/Garbage images captured and crowdsourced from over 2000+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at ****DC Labs.

Dataset Features

Dataset size : 9000+ Captured by : Over 2000+ crowdsource contributors Resolution : 99.9% images HD and above (1920x1080 and above) Location : Captured across 500+ cities Diversity : Various lighting conditions like day, night, varied distances, different material view points etc. Device used : Captured using mobile phones in 2020-2022 Usage : Trash detection, Material classification, Garbage segregation, Trash segregation, etc.

Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record

*To download full datasets or to submit a request for your dataset needs, please drop a mail on sales@datacluster.ai . Visit www.datacluster.ai to know more.

Overview

• Introduction
• Object Classes
  • Trash

Introduction

This dataset focuses on detecting trash in various environments to help improve waste management and environmental cleanliness. The primary class in this dataset is "trash." The dataset includes images from different settings such as grass, sand, and pavements.

Object Classes

Trash

Description

The "trash" class includes discarded items that are not part of the natural environment. These items can vary in shape and material, including but not limited to paper, plastic, and metal objects. They are often found on the ground and can be partially covered by surroundings like grass or sand.

Instructions

• General Instructions

  • Annotate all visible trash items regardless of size, ensuring the bounding box covers the entire object as closely as possible.
  • If an item is partially occluded, extend the box to include the occluded parts as best as you can infer their location.
  • Do not annotate natural elements like leaves or stones unless they are clearly attached to or entangled with trash.

• Detailed Visual Characteristics

  • Identify man-made items that differ from natural surroundings. Examples include crumpled plastic bags, disposable cups, cans, and wrappers.
  • Trash is often irregular in shape, contrasting with regular natural patterns like grass or tile.
  • When identifying trash on textured surfaces like grass or sand, look for sharp edges or shiny surfaces that catch light differently than natural materials.

• Specific Exclusions

  • Do not label areas where trash is not distinct or is too small to be effectively handled.
  • Avoid labeling objects that appear to be part of the background or are decorative elements that are intended to be part of the scene, such as a planter that is not overturned.

Always ensure that the annotations are precise and capture the full extent of the visible trash without overlapping with unrelated items.

For more information on the UAVVaste dataset, see the official repository or paper.

The dataset contains 772 aerial images from different heights and 3718 COCO-like annotations.

The main motivation for its creation was the lack of domain specific data for waste detection from drones or UAVs, making it a reference benchmark for object detection, but also for the development of UAV solutions, remote sensing or environmental cleaning.

modelling approaches need to include small object detection models suitable for real-time edge processing.

Waste disposal is a global challenge, especially in densely populated areas. Efficient waste segregation is critical for separating recyclable from non-recyclable materials. While developed countries have established and refined effective waste segmentation and recycling systems, our country still uses manual segregation to identify and process recyclable items. This study presents a dataset intended to improve automatic waste segmentation systems. The dataset consists of 784 images that have been manually annotated for waste classification. These images were primarily taken in and around Jadavpur University, including streets, parks, and lawns. Annotations were created with the Labelme program and are available in color annotation formats. The dataset includes 14 waste categories: plastic containers, plastic bottles, thermocol, metal bottles, plastic cardboard, glass, thermocol plates, plastic, paper, plastic cups, paper cups, aluminum foil, cloth, and nylon. The dataset includes a total of 2350 object segments.Other Information:Published in: Mendely DataLicense: http://creativecommons.org/licenses/by/4.0/See dataset on publisher's website: https://data.mendeley.com/datasets/gr99ny6b8p/1

This project uses data from:

• TACO - Trash Annotations in Context
  Author: Bruno Krauss
  License: Creative Commons Attribution 4.0
  Source: https://www.kaggle.com/datasets/BrunoKrauss/taco-trash-annotations-in-context

Merged and extended with custom images for the purposes of this project.

The authors released an open-source AquaTrash Dataset which consists of 369 images from 4 different categories related to various litter items. All the images in the AquaTrash dataset are manually annotated to obtain accuracy in the results. The dataset is based on the TACO data set.

Underwater Trash Detection Dataset Overview The Underwater Trash Detection Dataset is a custom-annotated dataset designed to address the challenges of underwater trash detection caused by varying environmental features. Publicly available datasets alone are insufficient for training deep learning models due to domain-specific variations in underwater conditions. This dataset offers a cumulative, self-annotated collection of underwater images for detecting and classifying trash, providing a strong foundation for deep learning research and benchmark testing.

Dataset Summary

Total Images: 9,576 Annotation Types: Trash classification (plastic, trash, underwater debris) vs. environmental factors (fish, flora, fauna).

Dataset Split | Split | Percentage | Number of Images | |-------------|----------------|-----------------------| | Train Set | 76% | 7,308 | | Validation | 19% | 1,795 | | Test Set | 5% | 473 |

Preprocessing

Image Resize: All images are resized to 256x256 pixels using stretching for uniform input dimensions.

Purpose This dataset supports research in underwater trash detection while addressing storage and computational constraints in underwater mobile devices. It enables the development of optimized algorithms for efficient trash detection and classification using minimal resources.

Citation If you use this dataset in your research, please cite:

@InProceedings{10.1007/978-3-031-43360-3_24,
author="Walia, Jaskaran Singh and Seemakurthy, Karthik",
editor="Iida, Fumiya
and Maiolino, Perla
and Abdulali, Arsen
and Wang, Mingfeng",
title="Optimized Custom Dataset for Efficient Detection of Underwater Trash",
booktitle="Towards Autonomous Robotic Systems",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="292--303",
}

Floating Trash Detection

## Overview

Floating Trash Detection is a dataset for object detection tasks - it contains Garbage Trash Floating annotations for 382 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Trash Dataset Train

## Overview

Trash Dataset Train is a dataset for object detection tasks - it contains Trashcans annotations for 2,578 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset is designed for training and evaluating object detection models focused on identifying various types of litter in real-world environments.

Dataset Overview:

Total Images: 1,499

Annotations: Each image is annotated with bounding boxes corresponding to different litter categories.

Classes: 59 distinct classes representing various waste items.

Dataset Split:

Training Set: 1,049 images (70%)

Validation Set: 299 images (20%)

Test Set: 151 images (10%)

Preprocessing:

Auto-Orient: Applied to ensure consistent image orientation.

Class Modification: 59 classes remapped; none dropped.

Augmentations: No augmentations were applied in this version.

This dataset is suitable for developing and testing object detection models aimed at recognizing and classifying litter in various settings, such as urban streets, parks, and natural environments. It can be instrumental in applications related to environmental monitoring, waste management, and sustainability initiatives.

TACO is a growing image dataset of waste in the wild. It contains images of litter taken under diverse environments: woods, roads and beaches. These images are manually labelled and segmented according to a hierarchical taxonomy to train and evaluate object detection algorithms. The annotations are provided in COCO format.

Garbage Annotation

## Overview

Garbage Annotation is a dataset for object detection tasks - it contains Garbage annotations for 462 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).