# CVX2

**Repository Path**: summry/cvx2

## Basic Information

- **Project Name**: CVX2
- **Description**: Tools for CV
- **Primary Language**: Python
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-11-12
- **Last Updated**: 2025-08-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Usage Sample
''''''''''''

.. code:: python

        from torch import nn
        from cvx2 import WidthBlock
        from cvx2.wrapper import ImageClassifyModelWrapper

        model = nn.Sequential(
            WidthBlock(c1=1, c2=32),
            nn.MaxPool2d(kernel_size=2, stride=2),
            WidthBlock(c1=32, c2=64),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Flatten(),
            nn.Linear(in_features=64*49, out_features=1024),
            nn.Dropout(0.2),
            nn.SiLU(inplace=True),
            nn.Linear(in_features=1024, out_features=2),
        )

        data_dir
         |__train
         |   |__class1
         |   |   |__001.jpg
         |   |   |__002.jpg
         |   |__class2
         |      |__001.jpg
         |      |__002.jpg
         |__test
         |   |__class1
         |   |   |__001.jpg
         |   |   |__002.jpg
         |   |__class2
         |      |__001.jpg
         |      |__002.jpg
         |__val
             |__class1
             |   |__001.jpg
             |   |__002.jpg
             |__class2
                |__001.jpg
                |__002.jpg

        model_wrapper = ImageClassifyModelWrapper(model)
        model_wrapper.train(data='data_dir', imgsz=28)
        result = model_wrapper.predict('data_dir/test/class1/001.jpg', imgsz=28)