CLIP-SatIR is a fine-tuned CLIP (ViT-B/32) model trained on the RSICD remote sensing dataset for cross-modal satellite image retrieval. The model aligns satellite imagery and natural language captions in a shared embedding space using contrastive learning.

Supports:

Text-to-image retrieval
Image-to-image similarity search
Semantic satellite search

Base Model

openai/clip-vit-base-patch32

Dataset

RSICD (Remote Sensing Image Caption Dataset) The dataset contains satellite imagery paired with descriptive captions covering:
airports
farmland
industrial zones
residential areas
rivers
urban layouts

Training Details

Objective

Contrastive CLIP loss: L = CrossEntropy(sim(image_i, text_j))

The model learns:

maximize similarity for matching pairs
minimize similarity for non-matching pairs

Hyperparameters

Parameter	Value
Batch Size	32
Learning Rate	1e-5
Optimizer	AdamW
Epochs	5
Hardware	CUDA GPU

Architecture

Vision Encoder → ViT-B/32
Text Encoder → Transformer
Shared 512-dimensional embedding space

Intended Use

This model is designed for:

Satellite image retrieval
Geospatial intelligence
Semantic satellite search
Urban planning analysis
Disaster monitoring

Limitations

Trained only on RSICD
Limited geographic diversity
Not suitable for critical surveillance decisions
Retrieval quality depends on caption semantics

Inference Example

python

from transformers import CLIPModel, CLIPProcessor
from PIL import Image
import torch

model = CLIPModel.from_pretrained("rishii100/clip-rsicd-finetuned")
processor = CLIPProcessor.from_pretrained("rishii100/clip-rsicd-finetuned")

text = ["an airport with multiple airplanes and runway"]

inputs = processor(text=text, return_tensors="pt", padding=True)

with torch.no_grad():
    text_features = model.get_text_features(**inputs)

print(text_features.shape)