OWL ViT vs. LLaVA: Compared and Contrasted

Compare OWL ViT and LLaVA-1.5 with Autodistill

Using Autodistill, you can compare OWL ViT and LLaVA on your own images in a few lines of code.

Here is an example comparison:

To start a comparison, first install the required dependencies:


pip install autodistill autodistill-owl-vit autodistill-llava

Next, create a new Python file and add the following code:


from autodistill_owl_vit import OWLViT
from autodistill_llava import LLaVA

from autodistill.detection import CaptionOntology
from autodistill.utils import compare

ontology = CaptionOntology(
    {
        "solar panel": "solar panel",
    }
)

models = [
    OWLViT(ontology=ontology),
    LLaVA(ontology=ontology)
]

images = [
    "/home/user/autodistill/solarpanel1.jpg",
    "/home/user/autodistill/solarpanel2.jpg"
]

compare(
    models=models,
    images=images
)

Above, replace the images in the `images` directory with the images you want to use.

The images must be absolute paths.

Then, run the script.

You should see a model comparison like this:

When you have chosen a model that works best for your use case, you can auto label a folder of images using the following code:


base_model.label(
  input_folder="./images",
  output_folder="./dataset",
  extension=".jpg"
)

‍

Models



OWL ViT vs. LLaVA

Both

OWL ViT

and

LLaVA-1.5

are commonly used in computer vision projects. Below, we compare and contrast

OWL ViT

and

LLaVA-1.5

	OWL ViT	LLaVA-1.5
Date of Release		Oct 05, 2023
Model Type	Object Detection	Object Detection
Architecture
GitHub Stars		16000

Using Autodistill, you can compare OWL ViT and LLaVA on your own images in a few lines of code.

Here is an example comparison:

To start a comparison, first install the required dependencies:


pip install autodistill autodistill-owl-vit autodistill-llava

Next, create a new Python file and add the following code:


from autodistill_owl_vit import OWLViT
from autodistill_llava import LLaVA

from autodistill.detection import CaptionOntology
from autodistill.utils import compare

ontology = CaptionOntology(
    {
        "solar panel": "solar panel",
    }
)

models = [
    OWLViT(ontology=ontology),
    LLaVA(ontology=ontology)
]

images = [
    "/home/user/autodistill/solarpanel1.jpg",
    "/home/user/autodistill/solarpanel2.jpg"
]

compare(
    models=models,
    images=images
)

Above, replace the images in the `images` directory with the images you want to use.

The images must be absolute paths.

Then, run the script.

You should see a model comparison like this:

When you have chosen a model that works best for your use case, you can auto label a folder of images using the following code:


base_model.label(
  input_folder="./images",
  output_folder="./dataset",
  extension=".jpg"
)

‍

OWL ViT

OWL-ViT is a transformer-based object detection model developed by Google Research.

How to Augment How to Label How to Plot Predictions How to Filter Predictions How to Create a Confusion Matrix

LLaVA-1.5

LLaVA is an open source multimodal language model that you can use for visual question answering and has limited support for object detection.

How to Augment How to Label How to Plot Predictions How to Filter Predictions How to Create a Confusion Matrix

Compare OWL ViT to other models

Compare LLaVA-1.5 to other models

Deploy a computer vision model today

Join 250,000 developers curating high quality datasets and deploying better models with Roboflow.

Get started

OWL ViT vs. LLaVA

Models

OWL ViT

LLaVA-1.5

Compare OWL ViT and LLaVA-1.5 with Autodistill

Explore Models

By Task

By Feature

OWL ViT vs. LLaVA

OWL ViT

LLaVA-1.5

Compare OWL ViT to other models

Compare LLaVA-1.5 to other models

Deploy a computer vision model today