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--- |
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license: apache-2.0 |
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datasets: |
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- prithivMLmods/Multilabel-GeoSceneNet-16K |
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library_name: transformers |
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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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tags: |
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- Structures |
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- Desert |
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- Glacier |
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- Street |
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- Ocean |
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- Image-Classifier |
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- art |
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- Mountain |
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--- |
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# **Multilabel-GeoSceneNet** |
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> **Multilabel-GeoSceneNet** is a vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for **multi-label** image classification. It is designed to recognize and label multiple geographic or environmental elements in a single image using the **SiglipForImageClassification** architecture. |
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```py |
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Classification Report: |
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precision recall f1-score support |
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Buildings and Structures 0.8881 0.9498 0.9179 2190 |
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Desert 0.9649 0.9480 0.9564 2000 |
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Forest Area 0.9807 0.9855 0.9831 2271 |
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Hill or Mountain 0.8616 0.8993 0.8800 2512 |
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Ice Glacier 0.9114 0.8382 0.8732 2404 |
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Sea or Ocean 0.9328 0.9525 0.9426 2274 |
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Street View 0.9476 0.9106 0.9287 2382 |
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accuracy 0.9245 16033 |
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macro avg 0.9267 0.9263 0.9260 16033 |
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weighted avg 0.9253 0.9245 0.9244 16033 |
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``` |
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--- |
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The model predicts the presence of one or more of the following **7 geographic scene categories**: |
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``` |
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Class 0: "Buildings and Structures" |
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Class 1: "Desert" |
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Class 2: "Forest Area" |
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Class 3: "Hill or Mountain" |
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Class 4: "Ice Glacier" |
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Class 5: "Sea or Ocean" |
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Class 6: "Street View" |
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``` |
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--- |
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## **Install dependencies** |
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```python |
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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/Multilabel-GeoSceneNet" # Updated model name |
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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 classify_geoscene_image(image): |
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"""Predicts geographic scene labels for an input 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.sigmoid(logits).squeeze().tolist() # Sigmoid for multilabel |
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labels = { |
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"0": "Buildings and Structures", |
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"1": "Desert", |
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"2": "Forest Area", |
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"3": "Hill or Mountain", |
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"4": "Ice Glacier", |
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"5": "Sea or Ocean", |
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"6": "Street View" |
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} |
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threshold = 0.5 |
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predictions = { |
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labels[str(i)]: round(probs[i], 3) |
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for i in range(len(probs)) if probs[i] >= threshold |
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} |
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return predictions or {"None Detected": 0.0} |
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# Create Gradio interface |
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iface = gr.Interface( |
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fn=classify_geoscene_image, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(label="Predicted Scene Categories"), |
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title="Multilabel-GeoSceneNet", |
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description="Upload an image to detect multiple geographic scene elements (e.g., forest, ocean, buildings)." |
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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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The **Multilabel-GeoSceneNet** model is suitable for recognizing multiple geographic and structural elements in a single image. Use cases include: |
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- **Remote Sensing:** Label elements in satellite or drone imagery. |
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- **Geographic Tagging:** Auto-tagging images for search or sorting. |
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- **Environmental Monitoring:** Identify features like glaciers or forests. |
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- **Scene Understanding:** Help autonomous systems interpret complex scenes. |
