Yolov5 support for Rikai

rikai-yolov5 integrates Yolov5 implemented in PyTorch with Rikai. It is based on the packaged ultralytics/yolov5.

Notebooks

• Using Rikai to analyze an image from Jay Chou's Mojito.

Usage

There are two ways to use rikai-yolov5.

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:

CREATE MODEL mlflow_yolov5_m
OPTIONS (
  device='cpu',
  iou_thres=0.5
)
USING 'mlflow:///{registered_model_name}';