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taco-trash-annotations-in-context/README.dataset.txt

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+# taco-trash-annotations-in-context-dtyly-awiq > 2025-03-13 10:38pm
+https://universe.roboflow.com/rf-100-vl/taco-trash-annotations-in-context-dtyly-awiq
+
+Provided by a Roboflow user
+License: MIT
+
+# Overview
+- [Introduction](#introduction)
+- [Object Classes](#object-classes)
+  - [Aluminium Foil](#aluminium-foil)
+  - [Broken Glass](#broken-glass)
+  - [Cigarette](#cigarette)
+  - [Clear Plastic Bottle](#clear-plastic-bottle)
+  - [Corrugated Carton](#corrugated-carton)
+  - [Disposable Plastic Cup](#disposable-plastic-cup)
+  - [Drink Can](#drink-can)
+  - [Glass Bottle](#glass-bottle)
+  - [Metal Bottle Cap](#metal-bottle-cap)
+  - [Normal Paper](#normal-paper)
+  - [Other Carton](#other-carton)
+  - [Other Plastic](#other-plastic)
+  - [Other Plastic Wrapper](#other-plastic-wrapper)
+  - [Paper Cup](#paper-cup)
+  - [Plastic Bottle Cap](#plastic-bottle-cap)
+  - [Plastic Film](#plastic-film)
+  - [Plastic Lid](#plastic-lid)
+  - [Plastic Straw](#plastic-straw)
+  - [Pop Tab](#pop-tab)
+  - [Single-use Carrier Bag](#single-use-carrier-bag)
+  - [Styrofoam Piece](#styrofoam-piece)
+  - [Unlabeled Litter](#unlabeled-litter)
+
+# Introduction
+This dataset is designed to aid in the identification and annotation of litter items commonly found in various environments. The goal is to provide comprehensive annotations that can be used to train machine learning models for object detection and classification. The dataset includes 22 classes, each representing a different type of litter.
+
+# Object Classes
+
+## Aluminium Foil
+### Description
+Thin, reflective material with a crumpled texture.
+
+### Instructions
+- Annotate the entire reflective area of the foil.
+- Include all visible crumpled parts.
+
+## Broken Glass
+### Description
+Sharp and translucent fragments of glass.
+
+### Instructions
+- Annotate each visible fragment.
+- Include all jagged edges.
+
+## Cigarette
+### Description
+Small cylindrical object with a distinct filter end.
+
+### Instructions
+- Annotate the entire visible length.
+- Do not include ash unless attached.
+
+## Clear Plastic Bottle
+### Description
+Transparent plastic bottle, typically with a label.
+
+### Instructions
+- Annotate the entire structure, including the neck.
+- Include visible labels.
+
+## Corrugated Carton
+### Description
+Brown, ribbed cardboard with a folded structure.
+
+### Instructions
+- Annotate all visible panels and folds.
+- Exclude minor tears.
+
+## Disposable Plastic Cup
+### Description
+Thin-walled plastic cup with a rim.
+
+### Instructions
+- Annotate the entire cup structure.
+- Include the rim if visible.
+
+## Drink Can
+### Description
+Metallic cylindrical can, often with a printed label.
+
+### Instructions
+- Annotate the full cylindrical shape.
+- Include both top and bottom ends if visible.
+
+## Glass Bottle
+### Description
+Thick-walled, transparent or colored bottle, usually with a narrow neck.
+
+### Instructions
+- Annotate the entire bottle structure.
+- Ignore minor cracks.
+
+## Metal Bottle Cap
+### Description
+Small, circular metal cap with ridged edges.
+
+### Instructions
+- Annotate the entire cap, emphasizing the ridged edges.
+- Exclude labels unless attached.
+
+## Normal Paper
+### Description
+Flat, thin sheet of white or printed paper.
+
+### Instructions
+- Annotate the whole visible area.
+- Exclude torn sections unless large.
+
+## Other Carton
+### Description
+Various shapes of paperboard, often colored or printed.
+
+### Instructions
+- Annotate the entire structure.
+- Include flaps if visible.
+
+## Other Plastic
+### Description
+Plastic items that are not bottles or cups, often irregular shapes.
+
+### Instructions
+- Annotate the full extent of each plastic piece.
+- Exclude extremely small fragments.
+
+## Other Plastic Wrapper
+### Description
+Thin, flexible plastic used for wrapping, often shiny.
+
+### Instructions
+- Annotate the entire wrapper including folds.
+- Include visible labels.
+
+## Paper Cup
+### Description
+Round cup made from paper, often with a printed design.
+
+### Instructions
+- Annotate the complete cup outline.
+- Include the rim and base.
+
+## Plastic Bottle Cap
+### Description
+Small, round plastic cap, often with text or logos.
+
+### Instructions
+- Annotate the entire cap, focusing on the circular shape.
+- Exclude loose text not part of the cap.
+
+## Plastic Film
+### Description
+Thin, transparent plastic often seen in sheets.
+
+### Instructions
+- Annotate the full visible film.
+- Exclude minor tears unless large.
+
+## Plastic Lid
+### Description
+Flat, round plastic disk used as a lid.
+
+### Instructions
+- Annotate the entire lid, emphasizing edges.
+
+## Plastic Straw
+### Description
+Long, thin plastic tube.
+
+### Instructions
+- Annotate the entire length of the straw.
+- Exclude crushed or bent sections.
+
+## Pop Tab
+### Description
+Metal pull tab from a can.
+
+### Instructions
+- Annotate the full tab, highlighting the ring.
+- Ignore attached fragments.
+
+## Single-use Carrier Bag
+### Description
+Thin plastic bag typically with handles.
+
+### Instructions
+- Annotate the full bag outline, including handles.
+- Include visible creases.
+
+## Styrofoam Piece
+### Description
+Foamy, lightweight material often in irregular shapes.
+
+### Instructions
+- Annotate the entire visible piece.
+- Ignore small detached fragments.
+
+## Unlabeled Litter
+### Description
+Miscellaneous waste items not fitting other categories.
+
+### Instructions
+- Annotate clearly visible litter.
+- Exclude ambiguous or unclear objects.

taco-trash-annotations-in-context/README.roboflow.txt

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+
+taco-trash-annotations-in-context-dtyly-awiq - v2 2025-03-13 10:38pm
+==============================
+
+This dataset was exported via roboflow.com on March 16, 2025 at 8:07 AM GMT
+
+Roboflow is an end-to-end computer vision platform that helps you
+* collaborate with your team on computer vision projects
+* collect & organize images
+* understand and search unstructured image data
+* annotate, and create datasets
+* export, train, and deploy computer vision models
+* use active learning to improve your dataset over time
+
+For state of the art Computer Vision training notebooks you can use with this dataset,
+visit https://github.com/roboflow/notebooks
+
+To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
+
+The dataset includes 1381 images.
+Taco-trash-annotations-in-context-dtyly-awiq are annotated in COCO format.
+
+The following pre-processing was applied to each image:
+
+No image augmentation techniques were applied.
+
+