VLPart vs. LLaVA: Compared and Contrasted

Compare VLPart and LLaVA-1.5 with Autodistill

Using Autodistill, you can compare VLPart 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-vlpart autodistill-llava

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


from autodistill_vlpart import VLPart
from autodistill_llava import LLaVA

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

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

models = [
    VLPart(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



VLPart vs. LLaVA

Both

VLPart

and

LLaVA-1.5

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

VLPart

and

LLaVA-1.5

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

Using Autodistill, you can compare VLPart 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-vlpart autodistill-llava

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


from autodistill_vlpart import VLPart
from autodistill_llava import LLaVA

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

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

models = [
    VLPart(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"
)

‍

VLPart

VLPart, developed by Meta Research, is an object detection and segmentation model that works with an open vocabulary

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 VLPart to other models

Compare LLaVA-1.5 to other models

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VLPart vs. LLaVA

Models

VLPart

LLaVA-1.5

Compare VLPart and LLaVA-1.5 with Autodistill

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VLPart vs. LLaVA

VLPart

LLaVA-1.5

Compare VLPart to other models

Compare LLaVA-1.5 to other models

Deploy a computer vision model today