File size: 3,110 Bytes
ba12fa0 9e0eccc a85aaba ba12fa0 29c1dcd a85aaba ba12fa0 a85aaba 29c1dcd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
---
license: apache-2.0
datasets:
- prithivMLmods/IndoorOutdoorNet-20K
library_name: transformers
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
tags:
- Indoor
- Outdoor
- Classification
- SigLIP2
---
# **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.
```py
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
```
---
The model categorizes images into 2 environment-related classes:
```
Class 0: "Indoor"
Class 1: "Outdoor"
```
---
## **Install dependencies**
```python
!pip install -q transformers torch pillow gradio
```
---
## **Inference Code**
```python
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()
```
---
## **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. |