Metadata-Version: 2.1
Name: rikai-yolov5
Version: 0.1.2
Summary: UNKNOWN
Home-page: https://github.com/Tubitv/rikai-yolov5
Author: Rikai authors
Author-email: rikai-dev@eto.ai
License: Apache License, Version 2.0
Description: # Yolov5 support for Rikai
        `rikai-yolov5` integrates Yolov5 implemented in PyTorch with Rikai. It is based
        on the [packaged ultralytics/yolov5](https://github.com/fcakyon/yolov5-pip).
        
        ## Notebooks
        + <a href="https://colab.research.google.com/github/eto-ai/rikai/blob/main/notebooks/Mojito.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> Using Rikai to analyze an image from Jay Chou's Mojito.
        
        ## Usage
        There are two ways to use `rikai-yolov5`.
        
        ``` python
        rikai.mlflow.pytorch.log_model(
            model,
            "model",
            OUTPUT_SCHEMA,
            registered_model_name=registered_model_name,
            model_type="yolov5",
        )
        ```
        
        Another way is setting the model_type in Rikai SQL:
        ```
        CREATE MODEL mlflow_yolov5_m
        MODEL_TYPE yolov5
        OPTIONS (
          device='cpu'
        )
        USING 'mlflow:///{registered_model_name}';
        ```
        
        ## Available Options
        
        | Name | Default Value | Description |
        |------|---------------|-------------|
        | conf_thres | 0.25 | NMS confidence threshold |
        | iou_thres  | 0.45 | NMS IoU threshold |
        | max_det    | 1000 | maximum number of detections per image |
        | image_size | 640  | Image width |
        
        Here is a sample usage of the above options:
        
        ``` sql
        CREATE MODEL mlflow_yolov5_m
        OPTIONS (
          device='cpu',
          iou_thres=0.5
        )
        USING 'mlflow:///{registered_model_name}';
        ```
        
Platform: UNKNOWN
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: dev
