Top Plastic Lid Datasets and Models
The datasets below can be used to train fine-tuned models for plastic lid 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 plastic lid datasets below.


TACO: Trash Annotations in Context Dataset


trash-detection


Trash Detection




project






Underwater Plastic Classification








Trash detector 2000


TACO: Object Detection










kona_2.11


Street Litter (Polygon)




TACO - detection


TACO - detection


TACO - segmentation


garbage detectorv7






yolov5-taco-fyp


20 Trash Detection


yolov5-taco-20


garbage detectorv2






sssssssssssssss


robolitter


stuff










Litter Detection


trash




TACO_data






11


Plastic Waste Detection


TACO dataset 2


TACO
Guide: How to Train a Computer Vision Model to Detect Plastic Lids
You can use datasets from Roboflow Universe to train a model to detect plastic lids 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 plastic lid project you choose above roboflow.download_dataset( dataset_url="https://universe.roboflow.com/mohamed-traore-2ekkp/taco-trash-annotations-in-context/16", 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 Plastic Lids 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.