Top Juice Datasets and Models

How to Train a Computer Vision Model to Detect Juice

You can use datasets from Roboflow Universe to train a model to detect juice 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 juice 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.

• Train a YOLOv8 Object Detection Model
• Train Another Computer Vision Model

Automatically Label Juice 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.

• Distill Large Vision Models into Smaller, Efficient Models with Autodistill (Object Detection Guide)
• Train an Image Classification Model with No Labeling