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--- |
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license: apache-2.0 |
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datasets: |
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- prithivMLmods/OpenScene-Classification |
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language: |
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- en |
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base_model: |
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- google/siglip-base-patch16-512 |
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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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- Scene-Detection |
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- buildings |
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- forest |
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- glacier |
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- mountain |
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- sea |
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- street |
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--- |
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# open-scene-detection |
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> open-scene-detection is a vision-language encoder model fine-tuned from [`siglip2-base-patch16-512`](https://huggingface.co/google/siglip-base-patch16-512) for multi-class scene classification. It is trained to recognize and categorize natural and urban scenes using a curated visual dataset. The model uses 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 0.9755 0.9570 0.9662 2625 |
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forest 0.9989 0.9955 0.9972 2694 |
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glacier 0.9564 0.9517 0.9540 2671 |
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mountain 0.9540 0.9592 0.9566 2723 |
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sea 0.9934 0.9898 0.9916 2758 |
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street 0.9595 0.9819 0.9706 2874 |
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accuracy 0.9728 16345 |
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macro avg 0.9730 0.9725 0.9727 16345 |
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weighted avg 0.9729 0.9728 0.9728 16345 |
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``` |
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--- |
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## Label Space: 6 Classes |
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The model classifies an image into one of the following scenes: |
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``` |
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Class 0: Buildings |
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Class 1: Forest |
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Class 2: Glacier |
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Class 3: Mountain |
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Class 4: Sea |
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Class 5: Street |
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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 hf_xet |
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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/open-scene-detection" # 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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# Updated label mapping |
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id2label = { |
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"0": "Buildings", |
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"1": "Forest", |
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"2": "Glacier", |
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"3": "Mountain", |
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"4": "Sea", |
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"5": "Street" |
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} |
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def classify_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_image, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(num_top_classes=6, label="Scene Classification"), |
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title="open-scene-detection", |
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description="Upload an image to classify the scene into one of six categories: Buildings, Forest, Glacier, Mountain, Sea, or Street." |
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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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`open-scene-detection` is designed for: |
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* **Scene Recognition** – Automatically classify natural and urban scenes. |
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* **Environmental Mapping** – Support geographic and ecological analysis from visual data. |
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* **Dataset Annotation** – Efficiently label large-scale image datasets by scene. |
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* **Visual Search and Organization** – Enable smart scene-based filtering or retrieval. |
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* **Autonomous Systems** – Assist navigation and perception modules with scene understanding. |
