Top Vegetable Datasets and Models
The datasets below can be used to train fine-tuned models for vegetable 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 vegetable datasets below.
How to Train a Computer Vision Model to Detect Vegetables
You can use datasets from Roboflow Universe to train a model to detect vegetables 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 vegetable project you choose above
roboflow.download_dataset(
dataset_url="https://universe.roboflow.com/{{WORKSPACE}}/food_recognition-efg2o/20",
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.
Automatically Label Vegetables 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.