---
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](https://github.com/fcakyon/yolov5-pip):

```bash
pip install -U yolov5
```

- Load model and perform prediction:

```python
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:

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