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---
license: apache-2.0
datasets:
- jonathan-roberts1/NWPU-RESISC45
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- RESISC45
- SigLIP2
- Image-Classification
---
# **RESISC45-SigLIP2**
> **RESISC45-SigLIP2** is a vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for **multi-label** image classification. It is specifically trained to recognize and tag multiple land use and land cover scene categories from the **RESISC45** dataset using the **SiglipForImageClassification** architecture.
> [!note]
*SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features* https://arxiv.org/pdf/2502.14786
```py
Classification Report:
precision recall f1-score support
airplane 0.9830 0.9900 0.9865 700
airport 0.9461 0.9529 0.9495 700
baseball diamond 0.9802 0.9886 0.9844 700
basketball court 0.9516 0.9271 0.9392 700
beach 0.9914 0.9900 0.9907 700
bridge 0.9730 0.9771 0.9751 700
chaparral 0.9957 0.9986 0.9971 700
church 0.7949 0.8971 0.8430 700
circular farmland 0.9914 0.9914 0.9914 700
cloud 0.9957 0.9871 0.9914 700
commercial area 0.9231 0.8229 0.8701 700
dense residential 0.9355 0.8914 0.9129 700
desert 0.9821 0.9414 0.9613 700
forest 0.9652 0.9514 0.9583 700
freeway 0.9344 0.9571 0.9457 700
golf course 0.9759 0.9843 0.9801 700
ground track field 0.9623 0.9857 0.9739 700
harbor 0.9885 0.9843 0.9864 700
industrial area 0.9505 0.9043 0.9268 700
intersection 0.9855 0.9686 0.9769 700
island 0.9871 0.9829 0.9850 700
lake 0.9440 0.9629 0.9533 700
meadow 0.9564 0.9400 0.9481 700
medium residential 0.8602 0.9314 0.8944 700
mobile home park 0.9610 0.9500 0.9555 700
mountain 0.9388 0.9429 0.9408 700
overpass 0.9614 0.9614 0.9614 700
palace 0.8455 0.8286 0.8369 700
parking lot 0.9899 0.9757 0.9827 700
railway 0.9407 0.9071 0.9236 700
railway station 0.9104 0.9143 0.9123 700
rectangular farmland 0.9572 0.9271 0.9419 700
river 0.9281 0.9586 0.9431 700
roundabout 0.9914 0.9871 0.9893 700
runway 0.9669 0.9586 0.9627 700
sea ice 0.9957 0.9943 0.9950 700
ship 0.9558 0.9886 0.9719 700
snowberg 0.9886 0.9900 0.9893 700
sparse residential 0.9238 0.9700 0.9463 700
stadium 0.9716 0.9757 0.9736 700
storage tank 0.9787 0.9829 0.9808 700
tennis court 0.9326 0.9486 0.9405 700
terrace 0.9372 0.9586 0.9477 700
thermal power station 0.9482 0.9671 0.9576 700
wetland 0.9444 0.8986 0.9209 700
accuracy 0.9532 31500
macro avg 0.9538 0.9532 0.9532 31500
weighted avg 0.9538 0.9532 0.9532 31500
```
---
## **Label Space: 45 Scene Categories**
The model predicts the presence of one or more of the following **45 scene categories**:
```
Class 0: "airplane"
Class 1: "airport"
Class 2: "baseball diamond"
Class 3: "basketball court"
Class 4: "beach"
Class 5: "bridge"
Class 6: "chaparral"
Class 7: "church"
Class 8: "circular farmland"
Class 9: "cloud"
Class 10: "commercial area"
Class 11: "dense residential"
Class 12: "desert"
Class 13: "forest"
Class 14: "freeway"
Class 15: "golf course"
Class 16: "ground track field"
Class 17: "harbor"
Class 18: "industrial area"
Class 19: "intersection"
Class 20: "island"
Class 21: "lake"
Class 22: "meadow"
Class 23: "medium residential"
Class 24: "mobile home park"
Class 25: "mountain"
Class 26: "overpass"
Class 27: "palace"
Class 28: "parking lot"
Class 29: "railway"
Class 30: "railway station"
Class 31: "rectangular farmland"
Class 32: "river"
Class 33: "roundabout"
Class 34: "runway"
Class 35: "sea ice"
Class 36: "ship"
Class 37: "snowberg"
Class 38: "sparse residential"
Class 39: "stadium"
Class 40: "storage tank"
Class 41: "tennis court"
Class 42: "terrace"
Class 43: "thermal power station"
Class 44: "wetland"
```
---
## **Install dependencies**
```bash
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/RESISC45-SigLIP2" # Update to your actual Hugging Face model path
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Label map
id2label = {
"0": "airplane", "1": "airport", "2": "baseball diamond", "3": "basketball court", "4": "beach",
"5": "bridge", "6": "chaparral", "7": "church", "8": "circular farmland", "9": "cloud",
"10": "commercial area", "11": "dense residential", "12": "desert", "13": "forest", "14": "freeway",
"15": "golf course", "16": "ground track field", "17": "harbor", "18": "industrial area", "19": "intersection",
"20": "island", "21": "lake", "22": "meadow", "23": "medium residential", "24": "mobile home park",
"25": "mountain", "26": "overpass", "27": "palace", "28": "parking lot", "29": "railway",
"30": "railway station", "31": "rectangular farmland", "32": "river", "33": "roundabout", "34": "runway",
"35": "sea ice", "36": "ship", "37": "snowberg", "38": "sparse residential", "39": "stadium",
"40": "storage tank", "41": "tennis court", "42": "terrace", "43": "thermal power station", "44": "wetland"
}
def classify_resisc_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.sigmoid(logits).squeeze().tolist()
threshold = 0.5
predictions = {
id2label[str(i)]: round(probs[i], 3)
for i in range(len(probs)) if probs[i] >= threshold
}
return predictions or {"None Detected": 0.0}
# Gradio Interface
iface = gr.Interface(
fn=classify_resisc_image,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Predicted Scene Categories"),
title="RESISC45-SigLIP2",
description="Upload a satellite image to detect multiple land use and land cover categories (e.g., airport, forest, mountain)."
)
if __name__ == "__main__":
iface.launch()
```
---
## **Intended Use**
The **RESISC45-SigLIP2** model is ideal for multi-label classification tasks involving remote sensing imagery. Use cases include:
- **Remote Sensing Analysis** – Label elements in aerial/satellite images.
- **Urban Planning** – Identify urban structures and landscape features.
- **Geospatial Intelligence** – Aid in automated image interpretation pipelines.
- **Environmental Monitoring** – Track natural landforms and changes. |