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---
license: apache-2.0
datasets:
- prithivMLmods/Math-Shapes
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- Shapes
- Geometric
- SigLIP2
- art
---
# **Geometric-Shapes-Classification**
> **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.
```py
Classification Report:
precision recall f1-score support
Circle ◯ 0.9921 0.9987 0.9953 1500
Kite ⬰ 0.9927 0.9927 0.9927 1500
Parallelogram ▰ 0.9926 0.9840 0.9883 1500
Rectangle ▭ 0.9993 0.9913 0.9953 1500
Rhombus ◆ 0.9846 0.9820 0.9833 1500
Square ◼ 0.9914 0.9987 0.9950 1500
Trapezoid ⏢ 0.9966 0.9793 0.9879 1500
Triangle ▲ 0.9772 0.9993 0.9881 1500
accuracy 0.9908 12000
macro avg 0.9908 0.9908 0.9907 12000
weighted avg 0.9908 0.9908 0.9907 12000
```
The model categorizes images into the following classes:
- **Class 0:** Circle ◯
- **Class 1:** Kite ⬰
- **Class 2:** Parallelogram ▰
- **Class 3:** Rectangle ▭
- **Class 4:** Rhombus ◆
- **Class 5:** Square ◼
- **Class 6:** Trapezoid ⏢
- **Class 7:** Triangle ▲
---
# **Run with Transformers 🤗**
```python
!pip install -q transformers torch pillow gradio
```
```python
import gradio as gr
from transformers import AutoImageProcessor
from transformers import SiglipForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/Geometric-Shapes-Classification"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Label mapping with symbols
labels = {
"0": "Circle ◯",
"1": "Kite ⬰",
"2": "Parallelogram ▰",
"3": "Rectangle ▭",
"4": "Rhombus ◆",
"5": "Square ◼",
"6": "Trapezoid ⏢",
"7": "Triangle ▲"
}
def classify_shape(image):
"""Classifies the geometric shape in the 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.nn.functional.softmax(logits, dim=1).squeeze().tolist()
predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
return predictions
# Gradio interface
iface = gr.Interface(
fn=classify_shape,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Prediction Scores"),
title="Geometric Shapes Classification",
description="Upload an image to classify geometric shapes such as circle, triangle, square, and more."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
```
---
# **Intended Use**
The **Geometric-Shapes-Classification** model is designed to recognize basic geometric shapes in images. Example use cases:
- **Educational Tools:** For learning and teaching geometry visually.
- **Computer Vision Projects:** As a shape detector in robotics or automation.
- **Image Analysis:** Recognizing symbols in diagrams or engineering drafts.
- **Assistive Technology:** Supporting shape identification for visually impaired applications. |