👁 scene.png

open-scene-detection

open-scene-detection is a vision-language encoder model fine-tuned from siglip2-base-patch16-512 for multi-class scene classification. It is trained to recognize and categorize natural and urban scenes using a curated visual dataset. The model uses the SiglipForImageClassification architecture.

Classification Report:
 precision recall f1-score support

 buildings 0.9755 0.9570 0.9662 2625
 forest 0.9989 0.9955 0.9972 2694
 glacier 0.9564 0.9517 0.9540 2671
 mountain 0.9540 0.9592 0.9566 2723
 sea 0.9934 0.9898 0.9916 2758
 street 0.9595 0.9819 0.9706 2874

 accuracy 0.9728 16345
 macro avg 0.9730 0.9725 0.9727 16345
weighted avg 0.9729 0.9728 0.9728 16345

👁 download.png

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Label Space: 6 Classes

The model classifies an image into one of the following scenes:

Class 0: Buildings 
Class 1: Forest 
Class 2: Glacier 
Class 3: Mountain 
Class 4: Sea 
Class 5: Street

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Install Dependencies

pip install -q transformers torch pillow gradio hf_xet

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Inference Code

import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/open-scene-detection" # Updated model name
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# Updated label mapping
id2label = {
 "0": "Buildings",
 "1": "Forest",
 "2": "Glacier",
 "3": "Mountain",
 "4": "Sea",
 "5": "Street"
}

def classify_image(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()

 prediction = {
 id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
 }

 return prediction

# Gradio Interface
iface = gr.Interface(
 fn=classify_image,
 inputs=gr.Image(type="numpy"),
 outputs=gr.Label(num_top_classes=6, label="Scene Classification"),
 title="open-scene-detection",
 description="Upload an image to classify the scene into one of six categories: Buildings, Forest, Glacier, Mountain, Sea, or Street."
)

if __name__ == "__main__":
 iface.launch()

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Intended Use

open-scene-detection is designed for:

• Scene Recognition – Automatically classify natural and urban scenes.
• Environmental Mapping – Support geographic and ecological analysis from visual data.
• Dataset Annotation – Efficiently label large-scale image datasets by scene.
• Visual Search and Organization – Enable smart scene-based filtering or retrieval.
• Autonomous Systems – Assist navigation and perception modules with scene understanding.

Base model

google/siglip-base-patch16-512

(3)

image detection • 3 items • Updated 1 day ago