import yolov5
model = yolov5.load('MBARI-org/megamidwater')

# Run the yolo
# set model parameters
model.conf = 0.25  # NMS confidence threshold
model.iou = 0.1  # NMS IoU threshold
model.agnostic = False  # NMS class-agnostic
model.multi_label = False  # NMS multiple labels per box
model.max_det = 1000  # maximum number of detections per image

# set image
img = 'http://dsg.mbari.org/images/dsg/external/Ctenophora/Deiopea_01.png'

# perform inference
results = model(img, size=1280)

# print results
print(results.pandas().xyxy[0])

Finetune the model on your custom dataset:

yolov5 train --data data.yaml --img 1280 --batch 16 --weights mbari-org/megamidwater --epochs 10