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How to use

  • Install yolov5:
pip install -U yolov5
  • Load model and perform prediction:
import yolov5

# load model
model = yolov5.load('sajinpgupta/smoke_detect')
# 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)

# inference with larger input size
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

# save results into "results/" folder
  • Finetune the model on your custom dataset:
yolov5 train --img 640 --batch 16 --weights sajinpgupta/smoke_detect --epochs 10 --device cuda:0
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Dataset used to train sajinpgupta/smoke_detect