Jonathancasjar/Retail_Shelves

• Install yolov5:

pip install yolov5==7.0.5

• Set image

wget -O 'image.jpg' 'https://images.unsplash.com/photo-1556767576-cf0a4a80e5b8?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxzZWFyY2h8NXx8c3VwZXJtYXJrZXQlMjBzaGVsdmVzfGVufDB8fDB8fHww&w=1000&q=80'

• Load model and perform prediction:

import yolov5

# load model
model = yolov5.load('Jonathancasjar/Retail_Shelves')

# set model parameters
model.conf = 0.25  # NMS confidence threshold

# set an image
img = '/content/image.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/')