OpenAI CLIP vs. MetaCLIP: Compared and Contrasted

Models

OpenAI CLIP

CLIP (Contrastive Language-Image Pre-Training) is an impressive multimodal zero-shot image classifier that achieves impressive results in a wide range of domains with no fine-tuning. It applies the recent advancements in large-scale transformers like GPT-3 to the vision arena.

Learn more about OpenAI CLIP

MetaCLIP

MetaCLIP is a zero-shot classification and embedding model developed by Meta AI.

Learn more about MetaCLIP

Model Type

Classification

Model Features

Item 1 Info

Item 2 Info

Architecture

CLIP

Frameworks

PyTorch

Annotation Format

Instance Segmentation

GitHub

View Repo

GitHub Stars

21.4k+

959+

License

MIT

CC BY-NC 4.0

Paper

View Paper

Training Notebook

Train on Colab

Deploy Model

Deploy with Roboflow

Compare Alternatives

Compare OpenAI CLIP and MetaCLIP with Autodistill

Using Autodistill, you can compare CLIP and MetaCLIP on your own images in a few lines of code.

First, install the required dependencies:


pip install autodistill autodistill-clip autodistill-metaclip

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


from autodistill_metaclip import MetaCLIP
from autodistill_clip import CLIP

from autodistill.detection import CaptionOntology

classes = ["solar panel", "building"]

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

metaclip_model = MetaCLIP(ontology=ontology)
clip_model = CLIP(ontology=ontology)

metaclip_results = metaclip_model.predict("./image.png", confidence=0)
clip_results = clip_model.predict("./image.png", confidence=0)

metaclip_classes = [classes[i] for i in metaclip_results.class_id.tolist()]
metaclip_scores = metaclip_results.confidence.tolist()

print("MetaCLIP Classes:", metaclip_classes)
print("MetaCLIP Scores:", metaclip_scores)

clip_classes = [classes[i] for i in clip_results.class_id.tolist()]
clip_scores = clip_results.confidence.tolist()

print("CLIP Classes:", clip_classes)
print("CLIP Scores:", clip_scores)

When you run the file, you will see an output that shows the results from the comparison:


MetaCLIP Classes: ['solar panel', 'building']
MetaCLIP Scores: [0.6350001096725464, 0.3649998903274536]
CLIP Classes: ['solar panel']
CLIP Scores: [0.6350001096725464]

Models



OpenAI CLIP vs. MetaCLIP

Both

OpenAI CLIP

and

MetaCLIP

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

OpenAI CLIP

and

MetaCLIP

	OpenAI CLIP	MetaCLIP
Date of Release	Jan 05, 2021	Sep 28, 2023
Model Type	Classification	Classification
Architecture		CLIP
GitHub Stars	21400	959

Using Autodistill, you can compare CLIP and MetaCLIP on your own images in a few lines of code.

First, install the required dependencies:


pip install autodistill autodistill-clip autodistill-metaclip

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


from autodistill_metaclip import MetaCLIP
from autodistill_clip import CLIP

from autodistill.detection import CaptionOntology

classes = ["solar panel", "building"]

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

metaclip_model = MetaCLIP(ontology=ontology)
clip_model = CLIP(ontology=ontology)

metaclip_results = metaclip_model.predict("./image.png", confidence=0)
clip_results = clip_model.predict("./image.png", confidence=0)

metaclip_classes = [classes[i] for i in metaclip_results.class_id.tolist()]
metaclip_scores = metaclip_results.confidence.tolist()

print("MetaCLIP Classes:", metaclip_classes)
print("MetaCLIP Scores:", metaclip_scores)

clip_classes = [classes[i] for i in clip_results.class_id.tolist()]
clip_scores = clip_results.confidence.tolist()

print("CLIP Classes:", clip_classes)
print("CLIP Scores:", clip_scores)

When you run the file, you will see an output that shows the results from the comparison:


MetaCLIP Classes: ['solar panel', 'building']
MetaCLIP Scores: [0.6350001096725464, 0.3649998903274536]
CLIP Classes: ['solar panel']
CLIP Scores: [0.6350001096725464]