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