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--- |
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license: apache-2.0 |
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datasets: |
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- prithivMLmods/Math-Shapes |
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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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- Shapes |
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- Geometric |
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- SigLIP2 |
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- art |
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--- |
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# **Geometric-Shapes-Classification** |
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> **Geometric-Shapes-Classification** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for a multi-class shape recognition task. It classifies various geometric shapes 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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Circle ◯ 0.9921 0.9987 0.9953 1500 |
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Kite ⬰ 0.9927 0.9927 0.9927 1500 |
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Parallelogram ▰ 0.9926 0.9840 0.9883 1500 |
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Rectangle ▭ 0.9993 0.9913 0.9953 1500 |
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Rhombus ◆ 0.9846 0.9820 0.9833 1500 |
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Square ◼ 0.9914 0.9987 0.9950 1500 |
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Trapezoid ⏢ 0.9966 0.9793 0.9879 1500 |
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Triangle ▲ 0.9772 0.9993 0.9881 1500 |
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accuracy 0.9908 12000 |
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macro avg 0.9908 0.9908 0.9907 12000 |
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weighted avg 0.9908 0.9908 0.9907 12000 |
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``` |
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The model categorizes images into the following classes: |
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- **Class 0:** Circle ◯ |
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- **Class 1:** Kite ⬰ |
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- **Class 2:** Parallelogram ▰ |
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- **Class 3:** Rectangle ▭ |
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- **Class 4:** Rhombus ◆ |
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- **Class 5:** Square ◼ |
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- **Class 6:** Trapezoid ⏢ |
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- **Class 7:** Triangle ▲ |
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--- |
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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 PIL import Image |
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import torch |
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# Load model and processor |
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model_name = "prithivMLmods/Geometric-Shapes-Classification" |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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# Label mapping with symbols |
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labels = { |
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"0": "Circle ◯", |
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"1": "Kite ⬰", |
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"2": "Parallelogram ▰", |
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"3": "Rectangle ▭", |
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"4": "Rhombus ◆", |
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"5": "Square ◼", |
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"6": "Trapezoid ⏢", |
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"7": "Triangle ▲" |
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} |
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def classify_shape(image): |
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"""Classifies the geometric shape in the 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.nn.functional.softmax(logits, dim=1).squeeze().tolist() |
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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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# Gradio interface |
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iface = gr.Interface( |
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fn=classify_shape, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(label="Prediction Scores"), |
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title="Geometric Shapes Classification", |
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description="Upload an image to classify geometric shapes such as circle, triangle, square, and more." |
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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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--- |
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# **Intended Use** |
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The **Geometric-Shapes-Classification** model is designed to recognize basic geometric shapes in images. Example use cases: |
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- **Educational Tools:** For learning and teaching geometry visually. |
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- **Computer Vision Projects:** As a shape detector in robotics or automation. |
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- **Image Analysis:** Recognizing symbols in diagrams or engineering drafts. |
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- **Assistive Technology:** Supporting shape identification for visually impaired applications. |
