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IndoorOutdoorNet

IndoorOutdoorNet is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to classify images as either Indoor or Outdoor using the SiglipForImageClassification architecture.

Classification Report:
 precision recall f1-score support

 Indoor 0.9661 0.9554 0.9607 9999
 Outdoor 0.9559 0.9665 0.9612 9999

 accuracy 0.9609 19998
 macro avg 0.9610 0.9609 0.9609 19998
weighted avg 0.9610 0.9609 0.9609 19998

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The model categorizes images into 2 environment-related classes:

 Class 0: "Indoor"
 Class 1: "Outdoor"

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Install dependencies

!pip install -q transformers torch pillow gradio

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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/IndoorOutdoorNet" # Updated model name
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

def classify_environment_image(image):
 """Predicts whether an image is Indoor or Outdoor."""
 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": "Indoor", "1": "Outdoor"
 }
 predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
 
 return predictions

# Create Gradio interface
iface = gr.Interface(
 fn=classify_environment_image,
 inputs=gr.Image(type="numpy"),
 outputs=gr.Label(label="Prediction Scores"),
 title="IndoorOutdoorNet",
 description="Upload an image to classify it as Indoor or Outdoor."
)

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

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Intended Use:

The IndoorOutdoorNet model is designed to classify images into indoor or outdoor environments. Potential use cases include:

• Smart Cameras: Detect indoor/outdoor context to adjust settings.
• Dataset Curation: Automatically filter image datasets by setting.
• Robotics & Drones: Environment-aware navigation logic.
• Content Filtering: Moderate or tag environment context in image platforms.

Base model

google/siglip2-base-patch16-224

(119)

Moderation, Balance, Classifiers • 5 items • Updated 1 day ago • 2