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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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- pytorch |
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library_name: yolov5 |
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library_version: 7.0.6 |
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inference: false |
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datasets: |
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- keremberke/license-plate-object-detection |
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model-index: |
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- name: keremberke/yolov5m-license-plate |
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results: |
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- task: |
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type: object-detection |
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dataset: |
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type: keremberke/license-plate-object-detection |
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name: keremberke/license-plate-object-detection |
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split: validation |
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metrics: |
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- type: precision |
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value: 0.9882982754936463 |
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name: mAP@0.5 |
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--- |
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<div align="center"> |
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<img width="640" alt="keremberke/yolov5m-license-plate" src="https://huggingface.co/keremberke/yolov5m-license-plate/resolve/main/sample_visuals.jpg"> |
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</div> |
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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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# load model |
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model = yolov5.load('keremberke/yolov5m-license-plate') |
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# set model parameters |
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model.conf = 0.25 # NMS confidence threshold |
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model.iou = 0.45 # 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 = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg' |
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# perform inference |
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results = model(img, size=640) |
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# inference with test time augmentation |
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results = model(img, augment=True) |
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# parse results |
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predictions = results.pred[0] |
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boxes = predictions[:, :4] # x1, y1, x2, y2 |
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scores = predictions[:, 4] |
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categories = predictions[:, 5] |
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# show detection bounding boxes on image |
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results.show() |
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# save results into "results/" folder |
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results.save(save_dir='results/') |
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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 640 --batch 16 --weights keremberke/yolov5m-license-plate --epochs 10 |
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``` |
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**More models available at: [awesome-yolov5-models](https://github.com/keremberke/awesome-yolov5-models)** |
