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--- |
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license: apache-2.0 |
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datasets: |
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- jonathan-roberts1/RSI-CB256 |
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language: |
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- en |
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base_model: |
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- google/siglip2-base-patch16-224 |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- Location |
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- RSI |
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- Remote Sensing Instruments |
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--- |
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# **RSI-CB256-35** |
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> **RSI-CB256-35** is a vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for **multi-class remote sensing image classification**. Built using the **SiglipForImageClassification** architecture, it is designed to accurately categorize overhead imagery into 35 distinct land-use and land-cover categories. |
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```py |
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Classification Report: |
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precision recall f1-score support |
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parking lot 0.9978 0.9872 0.9925 467 |
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avenue 0.9927 1.0000 0.9963 544 |
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highway 0.9283 0.9865 0.9565 223 |
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bridge 0.9283 0.9659 0.9467 469 |
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marina 0.9946 1.0000 0.9973 366 |
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crossroads 0.9909 0.9801 0.9855 553 |
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airport runway 0.9956 0.9926 0.9941 678 |
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pipeline 0.9900 1.0000 0.9950 198 |
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town 0.9970 1.0000 0.9985 335 |
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airplane 0.9915 0.9915 0.9915 351 |
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forest 0.9972 0.9945 0.9958 1082 |
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mangrove 1.0000 1.0000 1.0000 1049 |
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artificial grassland 0.9821 0.9717 0.9769 283 |
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river protection forest 1.0000 1.0000 1.0000 524 |
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shrubwood 1.0000 1.0000 1.0000 1331 |
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sapling 0.9955 1.0000 0.9977 879 |
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sparse forest 1.0000 1.0000 1.0000 1110 |
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lakeshore 1.0000 1.0000 1.0000 438 |
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river 0.9680 0.9555 0.9617 539 |
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stream 1.0000 0.9971 0.9985 688 |
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coastline 0.9913 0.9978 0.9946 459 |
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hirst 0.9890 1.0000 0.9945 628 |
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dam 0.9868 0.9259 0.9554 324 |
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sea 0.9971 0.9864 0.9917 1028 |
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snow mountain 1.0000 1.0000 1.0000 1153 |
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sandbeach 0.9944 0.9907 0.9925 536 |
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mountain 0.9926 0.9938 0.9932 812 |
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desert 0.9757 0.9927 0.9841 1092 |
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dry farm 1.0000 0.9992 0.9996 1309 |
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green farmland 0.9984 0.9969 0.9977 644 |
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bare land 0.9870 0.9630 0.9748 864 |
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city building 0.9785 0.9892 0.9838 1014 |
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residents 0.9926 0.9877 0.9901 810 |
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container 0.9970 0.9955 0.9962 660 |
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storage room 0.9985 1.0000 0.9992 1307 |
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accuracy 0.9919 24747 |
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macro avg 0.9894 0.9897 0.9895 24747 |
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weighted avg 0.9920 0.9919 0.9919 24747 |
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``` |
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--- |
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## **Label Space: 35 Remote Sensing Classes** |
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This model supports the classification of satellite or aerial images into the following classes: |
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``` |
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Class 0: "parking lot" |
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Class 1: "avenue" |
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Class 2: "highway" |
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Class 3: "bridge" |
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Class 4: "marina" |
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Class 5: "crossroads" |
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Class 6: "airport runway" |
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Class 7: "pipeline" |
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Class 8: "town" |
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Class 9: "airplane" |
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Class 10: "forest" |
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Class 11: "mangrove" |
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Class 12: "artificial grassland" |
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Class 13: "river protection forest" |
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Class 14: "shrubwood" |
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Class 15: "sapling" |
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Class 16: "sparse forest" |
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Class 17: "lakeshore" |
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Class 18: "river" |
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Class 19: "stream" |
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Class 20: "coastline" |
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Class 21: "hirst" |
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Class 22: "dam" |
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Class 23: "sea" |
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Class 24: "snow mountain" |
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Class 25: "sandbeach" |
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Class 26: "mountain" |
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Class 27: "desert" |
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Class 28: "dry farm" |
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Class 29: "green farmland" |
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Class 30: "bare land" |
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Class 31: "city building" |
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Class 32: "residents" |
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Class 33: "container" |
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Class 34: "storage room" |
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``` |
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--- |
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## **Install Dependencies** |
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```bash |
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pip install -q transformers torch pillow gradio |
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``` |
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--- |
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## **Inference Code** |
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```python |
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import gradio as gr |
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from transformers import AutoImageProcessor, SiglipForImageClassification |
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from PIL import Image |
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import torch |
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# Load model and processor |
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model_name = "prithivMLmods/RSI-CB256-35" |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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# ID to label mapping |
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id2label = { |
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"0": "parking lot", |
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"1": "avenue", |
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"2": "highway", |
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"3": "bridge", |
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"4": "marina", |
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"5": "crossroads", |
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"6": "airport runway", |
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"7": "pipeline", |
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"8": "town", |
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"9": "airplane", |
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"10": "forest", |
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"11": "mangrove", |
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"12": "artificial grassland", |
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"13": "river protection forest", |
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"14": "shrubwood", |
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"15": "sapling", |
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"16": "sparse forest", |
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"17": "lakeshore", |
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"18": "river", |
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"19": "stream", |
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"20": "coastline", |
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"21": "hirst", |
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"22": "dam", |
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"23": "sea", |
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"24": "snow mountain", |
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"25": "sandbeach", |
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"26": "mountain", |
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"27": "desert", |
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"28": "dry farm", |
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"29": "green farmland", |
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"30": "bare land", |
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"31": "city building", |
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"32": "residents", |
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"33": "container", |
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"34": "storage room" |
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} |
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def classify_rsi_image(image): |
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image = Image.fromarray(image).convert("RGB") |
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inputs = processor(images=image, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist() |
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prediction = { |
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id2label[str(i)]: round(probs[i], 3) for i in range(len(probs)) |
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} |
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return prediction |
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# Gradio Interface |
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iface = gr.Interface( |
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fn=classify_rsi_image, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(num_top_classes=5, label="Top-5 Predicted Categories"), |
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title="RSI-CB256-35", |
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description="Remote sensing image classification using SigLIP2. Upload an aerial or satellite image to classify its land-use category." |
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) |
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if __name__ == "__main__": |
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iface.launch() |
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``` |
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--- |
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## **Intended Use** |
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* **Land-Use Mapping and Planning** |
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* **Environmental Monitoring** |
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* **Infrastructure Identification** |
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* **Remote Sensing Analytics** |
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* **Agricultural and Forest Area Classification** |
