Models
Grounding DINO vs. LLaVA

Grounding DINO vs. LLaVA

Both and LLaVA-1.5 are commonly used in computer vision projects. Below, we compare and contrast and LLaVA-1.5.

Models

icon-model

icon-model

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.
Model Type
--
Object Detection
--
Model Features
Item 1 Info
Item 2 Info
Architecture
--
--
Frameworks
--
--
Annotation Format
Instance Segmentation
Instance Segmentation
GitHub Stars
--
16,000
--
License
--
Apache-2.0
--
Training Notebook
Compare Alternatives
--
Compare with...

Compare and LLaVA-1.5 with Autodistill

Using Autodistill, you can compare Grounding DINO 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-grounding-dino autodistill-llava

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


from autodistill_grounding_dino import GroundingDINO
from autodistill_llava import LLaVA

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

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

models = [
    GroundingDINO(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

Grounding DINO vs. LLaVA

.

Both

and

LLaVA-1.5

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

and

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

Using Autodistill, you can compare Grounding DINO 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-grounding-dino autodistill-llava

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


from autodistill_grounding_dino import GroundingDINO
from autodistill_llava import LLaVA

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

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

models = [
    GroundingDINO(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 AugmentHow to LabelHow to Plot PredictionsHow to Filter PredictionsHow to Create a Confusion Matrix

Compare to other models

No items found.

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