LLaVA vs. DETIC: Compared and Contrasted

Compare LLaVA-1.5 and DETIC with Autodistill

Using Autodistill, you can compare LLaVA and DETIC 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-llava autodistill-detic

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


from autodistill_llava import LLaVA
from autodistill_detic import DETIC

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

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

models = [
    LLaVA(ontology=ontology),
    DETIC(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



LLaVA vs. DETIC

Both

LLaVA-1.5

and

DETIC

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

LLaVA-1.5

and

DETIC

	LLaVA-1.5	DETIC
Date of Release	Oct 05, 2023	Jan 07, 2022
Model Type	Object Detection	Instance Segmentation
Architecture
GitHub Stars	16000	1400

Using Autodistill, you can compare LLaVA and DETIC 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-llava autodistill-detic

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


from autodistill_llava import LLaVA
from autodistill_detic import DETIC

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

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

models = [
    LLaVA(ontology=ontology),
    DETIC(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"
)

‍

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

DETIC

Detic is an open source segmentation model developed by Meta Research and released in 2022.

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

Compare LLaVA-1.5 to other models

Compare DETIC to other models

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LLaVA vs. DETIC

Models

LLaVA-1.5

DETIC

Compare LLaVA-1.5 and DETIC with Autodistill

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LLaVA vs. DETIC

LLaVA-1.5

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