Metadata-Version: 2.1
Name: bidcap
Version: 0.0.1
Summary: benchmark image dataset collection and preprocessing
Home-page: https://github.com/wwwbbb8510/bidcap.git
Author: Bin Wang
Author-email: wwwbbb8510@gmail.com
License: UNKNOWN
Description: # bidcap
        Benchmark Image Dataset Collection And Preprocessing
        
        ## Image datasets incorporated so far
        
        * MNIST dataset and its variants - 12000 train, 50000 test
            * MB: MNIST basic dataset
            * MBI: MNIST background image - A patch from a black and white image was used as the background for the digit image
            * MDRBI: MNIST digits with rotation and background image - The perturbations used in MRD and MBI were combined.
            * MRB: MNIST random background - A random background was inserted in the digit image
            * MRD: MNIST rotated digits - The digits were rotated by an angle generated uniformly between 0 and 360 radians.
            
        * CONVEX dataset - 8000 train, 50000 test
        
        ## Usage of the package
        
        ### download the datasets
        
        Download the datasets and put the files under the root directory of your project as shown in the following picture. 
        
        ![alt text](https://github.com/wwwbbb8510/bidcap/blob/master/dataset_file_structure.PNG "Datasets file structure")
        
        ### Load the datasets
        ```python
        # import the loader tool
        from bidcap.utils.loader import ImagesetLoader
        # import mb. Pass the dataset name described above as the first parameter
        data = ImagesetLoader.load('mb')
        # training images
        data.train['images']
        # training labels
        data.train['labels']
        # test images
        data.test['images']
        # test labels
        data.test['labels']
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
