Top Toilet Tube Datasets and Models
The datasets below can be used to train fine-tuned models for toilet tube 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 toilet tube datasets below.
Trash Detection
TACO: Trash Annotations in Context Dataset
trash-detection
TACO: Object Detection
Trash detector 2000
Trash 2.0
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
trash_segmentation
trash detect taco
ConvertCocoYolo
Taco Unofficial All Class
Taco to Yolo Final
class item
Trash Detection
TACO
LitterLocator - Home Project
Guide: How to Train a Computer Vision Model to Detect Toilet Tubes
You can use datasets from Roboflow Universe to train a model to detect toilet tubes 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 toilet tube 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 Toilet Tubes 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.

















































