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RSI-CB256-07

RSI-CB256-07 is a SigLIP2-based model fine-tuned for coarse-grained remote sensing land-cover classification. It distinguishes among 7 essential categories commonly used in environmental, urban planning, and geospatial analysis applications. The model is built on google/siglip2-base-patch16-224 using the SiglipForImageClassification architecture.

Classification Report:
 precision recall f1-score support

 transportation 0.9810 0.9858 0.9834 3300
 other objects 0.9854 0.9932 0.9893 884
 woodland 0.9973 0.9958 0.9966 6258
 water area 0.9870 0.9837 0.9854 4104
 other land 0.9925 0.9919 0.9922 3593
 cultivated land 0.9918 0.9901 0.9909 2817
construction land 0.9945 0.9963 0.9954 3791

 accuracy 0.9912 24747
 macro avg 0.9899 0.9910 0.9904 24747
 weighted avg 0.9912 0.9912 0.9912 24747

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Label Space: 7 Remote Sensing Classes

This model predicts one of the following categories for a given satellite or aerial image:

Class 0: "transportation" 
Class 1: "other objects" 
Class 2: "woodland" 
Class 3: "water area" 
Class 4: "other land" 
Class 5: "cultivated land" 
Class 6: "construction land"

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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/RSI-CB256-07"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# ID to label mapping
id2label = {
 "0": "transportation",
 "1": "other objects",
 "2": "woodland",
 "3": "water area",
 "4": "other land",
 "5": "cultivated land",
 "6": "construction land"
}

def classify_rsi_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_rsi_image,
 inputs=gr.Image(type="numpy"),
 outputs=gr.Label(num_top_classes=7, label="Predicted Land-Cover Category"),
 title="RSI-CB256-07",
 description="Upload a satellite or aerial image to classify it into one of seven coarse land-cover classes using SigLIP2."
)

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

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Applications

• Urban vs Rural Segmentation
• Land-Use Classification
• National/Regional Land Cover Monitoring
• Environmental Impact Assessment

Base model

google/siglip2-base-patch16-224

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