Metadata-Version: 2.1
Name: wk-classify
Version: 0.0.3.4
Summary: A package of tools for building deep-learning classification programs.
Home-page: https://github.com/Peiiii/wk-classify
Author: Wang Pei
Author-email: 1535376447@qq.com
License: UNKNOWN
Description: # wk-classify
        A package of tools for building deep-learning classification programs. Easy to use, light and powerful.
        
        # Install
        ```shell script
        pip3 install wk-classify
        ```
        
        # Usage
        
        ### Quick start
        ```python
        from wcf import train,  TrainValConfigBase
        class Config(TrainValConfigBase):
            TRAIN_DIR = 'path for train set'
            VAL_DIR = 'path for val set'
        cfg=Config()
        train(cfg)
        ```
        ### A real example
        ```python
        from wcf import train, TrainValConfigBase, val,t,EasyTransform,models_names
        class Config(TrainValConfigBase):
            MODEL_TYPE = models_names.shufflenet_v2_x0_5
            TAG = '[%s]'%(MODEL_TYPE)
            GEN_CLASSES_FILE = True
            USE_tqdm_TRAIN = True
            INPUT_SIZE = (252,196) #(w,h)
            BATCH_SIZE = 64
            MAX_EPOCHS = 200
            BALANCE_CLASSES = True
            VAL_INTERVAL = 1
            WEIGHTS_SAVE_INTERVAL = 1
            WEIGHTS_INIT = 'weights/training/model_best.pkl'
            TRAIN_DIR = '/home/ars/sda5/data/projects/烟盒/data/现场采集好坏烟照片/相机1-train'
            VAL_DIR = '/home/ars/sda5/data/projects/烟盒/data/现场采集好坏烟照片/相机1-val'
            val_transform = EasyTransform([
                t.Resize(INPUT_SIZE[::-1]),
                t.SaveToDir('data/test'),
                t.ToTensor(),
            ])
            train_transform = EasyTransform([
                t.ColorJitter(brightness=0.2, contrast=0, saturation=0, hue=0),
                # t.RandomHorizontalFlip(),
                # t.RandomVerticalFlip(),
                # t.RandomRotate(360),
                t.RandomTranslate(30),
                t.RandomBlur(p=0.3, radius=1),
                t.RandomSPNoise(p=0.3),
                *val_transform,
            ])
            # def get_model(self, num_classes=None):
                # model=YourModel(...)
                # return model
        
        
        if __name__ == '__main__':
            cfg = Config()
            train(cfg)
            # res=val(cfg)
            # print(res)
        
        ```
        
        ### all options
        see the `TrainValConfigBase` class for all options
        
        ### how to predict?
        see `demo_predict.py`
        
        ## more
        
        see `demo_train.py` and  `demo_predict.py`
        
        
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.0
Description-Content-Type: text/markdown
