def resNet_data_preprocess(color_img):
    """

        All pre-trained models expect input images normalized in the same way, 
        i.e. mini-batches of 3-channel RGB images of shape (3 x H x W), where 
        H and W are expected to be atleast 224. The images have to be loaded in 
        to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 
        0.406] and std = [0.229, 0.224, 0.225]

        input[channel] = (input[channel] - mean[channel]) / std[channel]
        Args:
            color_img should be a float32 numpy array of 3 x H x W 

    """    
    mean = [0.485, 0.456, 0.406]
    std = [0.229, 0.224, 0.225]

    for channel in range(3):
        color_img[channel, :, :] -= mean[channel]
        color_img[channel, :, :] /= std[channel]

    return color_img