OpenCV’s cv2.dnn.readNetFromTensorflow function requires two files: the and a text graph (.pbtxt) . The .pbtxt file acts as a configuration that helps OpenCV understand the network structure.
import cv2 # Define paths to the downloaded model files model_weights = "frozen_inference_graph.pb" model_config = "ssd_mobilenet_v2_coco.pbtxt" # Load the network net = cv2.dnn.readNetFromTensorflow(model_weights, model_config) # Prepare an image for inference image = cv2.imread("input.jpg") blob = cv2.dnn.blobFromImage(image, size=(300, 300), swapRB=True, crop=False) net.setInput(blob) # Run detection output = net.forward() Use code with caution. Essential Requirements & Troubleshooting frozen_inference_graph.pb opencv download
The frozen_inference_graph.pb file is a serialized TensorFlow model that contains the pre-trained weights and graph definition of a neural network. For developers using the , downloading and correctly loading these files is a standard way to implement real-time object detection and image segmentation without needing to train a model from scratch. Where to Download frozen_inference_graph.pb OpenCV’s cv2
Note: After downloading the .tar.gz file, you must extract it to find the frozen_inference_graph.pb file inside. Loading the Model in OpenCV Loading the Model in OpenCV