OWLv2 vs. LLaVA: Compared and Contrasted

Compare OWLv2 and LLaVA-1.5 with Autodistill

Using Autodistill, you can compare OWLv2 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-owlv2 autodistill-llava

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


from autodistill_owlv2 import OWLv2
from autodistill_llava import LLaVA

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

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

models = [
    OWLv2(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



OWLv2 vs. LLaVA

Both

OWLv2

and

LLaVA-1.5

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

OWLv2

and

LLaVA-1.5

	OWLv2	LLaVA-1.5
Date of Release		Oct 05, 2023
Model Type	Object Detection	Object Detection
Architecture
GitHub Stars		16000

Using Autodistill, you can compare OWLv2 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-owlv2 autodistill-llava

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


from autodistill_owlv2 import OWLv2
from autodistill_llava import LLaVA

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

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

models = [
    OWLv2(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"
)

OWLv2

OWLv2 is a transformer-based object detection model developed by Google Research. OWLv2 is the successor to OWL ViT.

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 OWLv2 to other models

CoDet

Kosmos-2

LLaVA-1.5

Compare LLaVA-1.5 to other models

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OWLv2 vs. LLaVA

Models

OWLv2

LLaVA-1.5

Compare OWLv2 and LLaVA-1.5 with Autodistill

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OWLv2 vs. LLaVA

OWLv2

LLaVA-1.5

Compare OWLv2 to other models

Compare LLaVA-1.5 to other models

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