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--- |
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license: apache-2.0 |
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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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- siglip2 |
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- SAT |
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- Landforms |
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--- |
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# **SAT-Landforms-Classifier** |
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> **SAT-Landforms-Classifier** 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 satellite images into different landform categories using the **SiglipForImageClassification** architecture. |
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```py |
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Accuracy: 0.9863 |
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F1 Score: 0.9858 |
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Classification Report: |
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precision recall f1-score support |
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Annual Crop 0.9866 0.9810 0.9838 3000 |
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Forest 0.9927 0.9957 0.9942 3000 |
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Herbaceous Vegetation 0.9697 0.9800 0.9748 3000 |
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Highway 0.9826 0.9928 0.9877 2500 |
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Industrial 0.9964 0.9916 0.9940 2500 |
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Pasture 0.9882 0.9610 0.9744 2000 |
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Permanent Crop 0.9690 0.9760 0.9725 2500 |
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Residential 0.9940 0.9970 0.9955 3000 |
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River 0.9864 0.9872 0.9868 2500 |
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Sea Lake 0.9963 0.9923 0.9943 3000 |
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accuracy 0.9863 27000 |
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macro avg 0.9862 0.9855 0.9858 27000 |
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weighted avg 0.9863 0.9863 0.9863 27000 |
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``` |
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The model categorizes images into ten classes: |
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- **Class 0:** "Annual Crop" |
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- **Class 1:** "Forest" |
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- **Class 2:** "Herbaceous Vegetation" |
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- **Class 3:** "Highway" |
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- **Class 4:** "Industrial" |
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- **Class 5:** "Pasture" |
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- **Class 6:** "Permanent Crop" |
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- **Class 7:** "Residential" |
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- **Class 8:** "River" |
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- **Class 9:** "Sea Lake" |
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# **Run with Transformers🤗** |
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```python |
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!pip install -q transformers torch pillow gradio |
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``` |
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```python |
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import gradio as gr |
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from transformers import AutoImageProcessor |
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from transformers import SiglipForImageClassification |
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from transformers.image_utils import load_image |
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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/SAT-Landforms-Classifier" |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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def landform_classification(image): |
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"""Predicts landform category for a satellite 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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labels = { |
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"0": "Annual Crop", "1": "Forest", "2": "Herbaceous Vegetation", "3": "Highway", "4": "Industrial", |
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"5": "Pasture", "6": "Permanent Crop", "7": "Residential", "8": "River", "9": "Sea Lake" |
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} |
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predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))} |
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return predictions |
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# Create Gradio interface |
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iface = gr.Interface( |
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fn=landform_classification, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(label="Prediction Scores"), |
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title="SAT Landforms Classification", |
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description="Upload a satellite image to classify its landform type." |
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) |
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# Launch the app |
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if __name__ == "__main__": |
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iface.launch() |
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``` |
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# **Intended Use:** |
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The **SAT-Landforms-Classifier** model is designed to classify satellite images into various landform types. Potential use cases include: |
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- **Land Use Monitoring:** Identifying different land use patterns from satellite imagery. |
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- **Environmental Studies:** Supporting researchers in tracking changes in vegetation and water bodies. |
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- **Urban Planning:** Assisting planners in analyzing residential, industrial, and infrastructure distributions. |
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- **Agricultural Analysis:** Helping assess crop distribution and pastureland areas. |
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- **Disaster Management:** Providing insights into land coverage for emergency response and recovery planning. |
