metadata
tags:
- yolov5
- yolo
- vision
- object-detection
- biology
- climate
library_name: yolov5
library_version: 7.0.7
inference: false
model-index:
- name: mbari-org/megamidwater
results:
- task:
type: object-detection
metrics:
- type: precision
value: 0.73555
name: mAP@0.5
license: apache-2.0
language:
- en
pipeline_tag: object-detection
How to use
- Install yolov5:
pip install -U yolov5
- Load model and perform prediction:
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