keras-retinanet | Keras implementation of RetinaNet object detection | Machine Learning library

from core import Core  

c = Core()  
  
image_filename = c.current_path + "/DataSets/Drones/testImages/351.jpg"  
image = c.load_image_by_path(image_filename)  
  
drawing_image = c.get_drawing_image(image)  
  
processed_image, scale = c.pre_proces  

from core import Core  

c = Core()  
  
image_filename = c.current_path + "/DataSets/Drones/testImages/351.jpg"  
image = c.load_image_by_path(image_filename)  
  
drawing_image = c.get_drawing_image(image)  
  
processed_image, scale = c.pre_proces  

#!/bin/bash  
echo "Checking for CUDA and installing."  
# Check for CUDA and try to install.  
if ! dpkg-query -W cuda; then  
  # The 16.04 installer works with 16.10.  
  curl -O http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x  

for file in os.listdir("/content/test"):
  print('running test on: ' + file)
  img_infer = list(file)[0] # <== This line is not correct
  img_inference(img_infer)

for file in os.listdir("/content/test"):
  print

gamma = 2
alpha2 = 0.25
preds = np.array(
    [[0.3, 0.2, 0.5], [0.1, 0.4, 0.4], [0.1, 0.8, 0.1], 
     [0.7, 0.2, 0.1], [0.75, 0.15, 0.1], [0.9, 0.05, 0.05],
     [0.72, 0.18, 0.1], [0.8, 0.1, 0.1]])
gt = np.array([[1, 0, 0], [0, 1, 0], [

    This will create the virtual environment. Make sure to name it venv because the .gitignore file
    has been initialized to ignore it by default. 

    This will go to the requirements file that is already gener