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--- |
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tags: |
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- yolov5 |
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- yolo |
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- vision |
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- object-detection |
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- biology |
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- climate |
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library_name: yolov5 |
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library_version: 7.0.7 |
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inference: false |
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model-index: |
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- name: mbari-org/megamidwater |
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results: |
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- task: |
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type: object-detection |
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metrics: |
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- type: precision |
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value: 0.73555 |
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name: [email protected] |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: object-detection |
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--- |
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### How to use |
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- Install [yolov5](https://github.com/fcakyon/yolov5-pip): |
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```bash |
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pip install -U yolov5 |
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``` |
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- Load model and perform prediction: |
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```python |
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import yolov5 |
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model = yolov5.load('MBARI-org/megamidwater') |
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# Run the yolo |
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# set model parameters |
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model.conf = 0.25 # NMS confidence threshold |
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model.iou = 0.1 # NMS IoU threshold |
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model.agnostic = False # NMS class-agnostic |
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model.multi_label = False # NMS multiple labels per box |
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model.max_det = 1000 # maximum number of detections per image |
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# set image |
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img = 'http://dsg.mbari.org/images/dsg/external/Ctenophora/Deiopea_01.png' |
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# perform inference |
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results = model(img, size=1280) |
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# print results |
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print(results.pandas().xyxy[0]) |
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``` |
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- Finetune the model on your custom dataset: |
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```bash |
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yolov5 train --data data.yaml --img 1280 --batch 16 --weights mbari-org/megamidwater --epochs 10 |
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``` |
