Top Wrapping Paper Datasets and Models
The datasets below can be used to train fine-tuned models for wrapping paper detection. You can explore each dataset in your browser using Roboflow and export the dataset into one of many formats.
At the bottom of this page, we have guides on how to train a model using the wrapping paper datasets below.
Trash Detection
TACO: Trash Annotations in Context Dataset
trash-detection
TACO: Object Detection
Trash detector 2000
Trash 2.0
project
Waste Detection
Trash Detection2
TACO dataset
trash
TACO dataset
TACO
Waste detection
Underwater Plastic Classification
Litter Detection
trash_object_detection
trash
gc
garbage detectorv7
FYP
trash_1
Trash-Detection-V3
trash_taco_archisman
Taco garbage
TACO_v2
2
trash_segmentation
trash detect taco
4
ConvertCocoYolo
Taco Unofficial All Class
Taco to Yolo Final
Trash Detection
Trash
TACO
Guide: How to Train a Computer Vision Model to Detect Wrapping Papers
You can use datasets from Roboflow Universe to train a model to detect wrapping papers in images and videos.
To download a dataset, first install the Roboflow Python package (pip install roboflow), then then the following code snippet.
When you run the code for the first time, you will be asked to authenticate with Roboflow.
import roboflow
roboflow.login()
# replace with the wrapping paper project you choose above
roboflow.download_dataset(
dataset_url="https://universe.roboflow.com/trash-dataset-for-oriented-bounded-box/trash-detection-1fjjc/14",
model_format="coco"
)
Where dataset_url is set to a project and version in the dataset you choose from the results above.
Roboflow has written guides on how to train computer vision models with popular architectures. Many guides come with accompanying notebooks you can follow to train a model.
Guide: Automatically Label Wrapping Papers in an Unlabeled Dataset
You can use foundation models to automatically label data using Autodistill.
Autodistill supports using many state-of-the-art models like Grounding DINO and Segment Anything to auto-label data. This is useful if a dataset you want to use is not already labeled.
Autodistill performs well at identifying common objects, but may struggle with more obscure objects. We recommend trying Autodistill using Grounded SAM for detection and segmentation or CLIP for classification.
Follow our guides below to get started.

















































