README.md

metadata

license: afl-3.0

---

tags:

• yolov5
• yolo
• vision
• object-detection
• pytorch library_name: yolov5 library_version: 7.0.6 inference: false

datasets:

• keremberke/license-plate-object-detection

model-index:

• name: keremberke/yolov5m-license-plate results:

  • task: type: object-detection

    dataset: type: keremberke/license-plate-object-detection name: keremberke/license-plate-object-detection split: validation

    metrics:

    • type: precision # since mAP@0.5 is not available on hf.co/metrics value: 0.9882982754936463 # min: 0.0 - max: 1.0 name: mAP@0.5

---

pip install -U yolov5

• Load model and perform prediction:

import yolov5

# load model
model = yolov5.load('keremberke/yolov5m-license-plate')
  
# set model parameters
model.conf = 0.25  # NMS confidence threshold
model.iou = 0.45  # 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 = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'

# perform inference
results = model(img, size=640)

# inference with test time augmentation
results = model(img, augment=True)

# parse results
predictions = results.pred[0]
boxes = predictions[:, :4] # x1, y1, x2, y2
scores = predictions[:, 4]
categories = predictions[:, 5]

# show detection bounding boxes on image
results.show()

# save results into "results/" folder
results.save(save_dir='results/')

• Finetune the model on your custom dataset:

yolov5 train --data data.yaml --img 640 --batch 16 --weights keremberke/yolov5m-license-plate --epochs 10

*More models available at: awesome-yolov5-models