Geometric Shapes Classification

🚀 幾何形狀分類模型

本項目的幾何形狀分類模型是一個圖像分類的視覺語言編碼器模型，它基於 google/siglip2-base-patch16-224 進行微調，用於多類形狀識別任務。該模型使用 SiglipForImageClassification 架構對各種幾何形狀進行分類。

🚀 快速開始

安裝依賴

!pip install -q transformers torch pillow gradio

運行代碼

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()

💻 使用示例

基礎用法

# 以下代碼展示瞭如何使用該模型進行幾何形狀分類
import gradio as gr
from transformers import AutoImageProcessor
from transformers import SiglipForImageClassification
from PIL import Image
import torch

# 加載模型和處理器
model_name = "prithivMLmods/Geometric-Shapes-Classification"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# 帶有符號的標籤映射
labels = {
    "0": "Circle ◯",
    "1": "Kite ⬰",
    "2": "Parallelogram ▰",
    "3": "Rectangle ▭",
    "4": "Rhombus ◆",
    "5": "Square ◼",
    "6": "Trapezoid ⏢",
    "7": "Triangle ▲"
}

def classify_shape(image):
    """對輸入圖像中的幾何形狀進行分類。"""
    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 界面
iface = gr.Interface(
    fn=classify_shape,
    inputs=gr.Image(type="numpy"),
    outputs=gr.Label(label="Prediction Scores"),
    title="Geometric Shapes Classification",
    description="上傳一張圖像，對圓形、三角形、正方形等幾何形狀進行分類。"
)

# 啟動應用
if __name__ == "__main__":
    iface.launch()

📚 詳細文檔

分類報告

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

模型分類的類別

該模型將圖像分類為以下類別：

• 類別 0：圓形 ◯
• 類別 1：風箏形 ⬰
• 類別 2：平行四邊形 ▰
• 類別 3：矩形 ▭
• 類別 4：菱形 ◆
• 類別 5：正方形 ◼
• 類別 6：梯形 ⏢
• 類別 7：三角形 ▲

預期用途

幾何形狀分類 模型旨在識別圖像中的基本幾何形狀。示例用例如下：

• 教育工具：用於以可視化方式學習和教授幾何知識。
• 計算機視覺項目：作為機器人或自動化中的形狀檢測器。
• 圖像分析：識別圖表或工程圖紙中的符號。
• 輔助技術：支持視障應用中的形狀識別。

📄 許可證

本項目採用 Apache-2.0 許可證。

📦 模型信息