• Hardware (V100)
• Network Type (Detectnet_v2)
• Training spec file
import cv2
import numpy as np
labels = [“Car”,“Bicycle”,“Person”,“Roadsign”]
MODEL_FILE = ‘resnet10.prototxt’
PRETRAINED = ‘resnet10.caffemodel’
load DNN model
nn = cv2.dnn.readNetFromCaffe(MODEL_FILE,PRETRAINED)
print(“[Status] loading model”)
read image from disk
image_name = ‘208_212_1631260113.jpg’
image = cv2.imread(image_name)
image_height, image_width, _ = image.shape
print(f"[Status] loading image")
print(f"Image resolution: {image.shape}")
create blob from image
print(f"[Status] creating blob from image")
blob = cv2.dnn.blobFromImage(
image, ### image
1, ### scalefactor
size = (640,368), ### size required by model
mean=(104, 117, 123), ### mean subtraction
swapRB = True)
Run inference
nn.setInput(blob)
output = nn.forward())
I am loading the caffe model with OpenCV (code above) to run inference on an image. I am resizing the image to 640,368 as mentioned in prototxt file. Once run the inference, the output is an array of dimensions 1X4X23X40. Is this the coverage map? Where can I find the bboxes? Thank u!