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MetaCLIP-2-Open-Scene

MetaCLIP-2-Open-Scene is an image classification vision-language encoder model fine-tuned from facebook/metaclip-2-worldwide-s16 for a single-label classification task. It is designed to identify and categorize various natural and urban scenes using the MetaClip2ForImageClassification architecture.

MetaCLIP 2: A Worldwide Scaling Recipe : https://huggingface.co/papers/2507.22062

Classification Report:
 precision recall f1-score support

 buildings 0.9644 0.9703 0.9673 2625
 forest 0.9948 0.9978 0.9963 2694
 glacier 0.9531 0.9427 0.9479 2671
 mountain 0.9470 0.9512 0.9491 2723
 sea 0.9909 0.9920 0.9915 2758
 street 0.9728 0.9694 0.9711 2874

 accuracy 0.9706 16345
 macro avg 0.9705 0.9706 0.9705 16345
weighted avg 0.9706 0.9706 0.9706 16345

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The model classifies images into six open-scene categories:

• Class 0: "buildings"
• Class 1: "forest"
• Class 2: "glacier"
• Class 3: "mountain"
• Class 4: "sea"
• Class 5: "street"

Run with Transformers

!pip install -q transformers torch pillow gradio

import gradio as gr
from transformers import AutoImageProcessor
from transformers import AutoModelForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/MetaCLIP-2-Open-Scene"
model = AutoModelForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

def scene_classification(image):
 """Predicts the type of scene represented in an image."""
 image = Image.fromarray(image).convert("RGB")
 inputs = processor(images=image, return_tensors="pt")

 with torch.no_grad():
 outputs = model(**inputs)
 logits = outputs.logits
 probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()

 labels = {
 "0": "buildings",
 "1": "forest",
 "2": "glacier",
 "3": "mountain",
 "4": "sea",
 "5": "street"
 }
 predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}

 return predictions

# Create Gradio interface
iface = gr.Interface(
 fn=scene_classification,
 inputs=gr.Image(type="numpy"),
 outputs=gr.Label(label="Prediction Scores"),
 title="Open Scene Classification",
 description="Upload an image to classify the scene type (e.g., forest, sea, street, mountain, etc.)."
)

# Launch the app
if __name__ == "__main__":
 iface.launch()

Sample Inference:

👁 Screenshot 2025-11-13 at 19-39-55 Open Scene Classification
👁 Screenshot 2025-11-13 at 19-37-07 Open Scene Classification
👁 Screenshot 2025-11-13 at 19-37-50 Open Scene Classification
👁 Screenshot 2025-11-13 at 19-38-37 Open Scene Classification
👁 Screenshot 2025-11-13 at 19-39-24 Open Scene Classification

Intended Use:

The MetaCLIP-2-Open-Scene model is designed to classify a wide range of natural and urban environments. Potential use cases include:

• Geographical Image Analysis: Categorizing landscapes for environmental and mapping studies.
• Tourism and Travel Applications: Automatically tagging scenic photos for organization and recommendations.
• Autonomous Systems: Supporting navigation and perception in robotics and self-driving systems.
• Environmental Monitoring: Detecting and classifying geographic features for research.
• Media and Photography: Assisting in photo organization and content-based retrieval.

Base model