Issue with using SSD in deepface.analyze(): ‘no attribute ‘readNetFromCaffe’

What will you learn?

In this tutorial, you will learn how to troubleshoot the error ‘no attribute ‘readNetFromCaffe’ that occurs when utilizing SSD in deepface.analyze().

Introduction to the Problem and Solution

Encountering the error message ‘no attribute ‘readNetFromCaffe’ within OpenCV’s cv2.dnn module while working with SSD (Single Shot MultiBox Detector) in deepface.analyze() signifies that the method is not present in the specified library. To overcome this issue, it is necessary to employ an alternative method called cv2.dnn.readNet instead of cv2.dnn.readNetFromCaffe. By making this adjustment, you can effectively leverage SSD for face analysis tasks.


# Import required libraries
import cv2

# Load pre-trained weights and model configuration for face detection using SSD 
net = cv2.dnn.readNet("weights.caffemodel", "configuration.prototxt")

# Copyright PHD

(Ensure to replace “weights.caffemodel” and “configuration.prototxt” with your actual file paths)

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The provided code snippet begins by importing OpenCV as cv2 library, which offers functionalities for computer vision applications. Subsequently, the cv2.dnn.readNet method is utilized to load pre-trained weights (weights.caffemodel) and model configuration (configuration.prototxt) essential for SSD in face detection. This modification from cv2.dnn.readNetFromCaffe resolves the previously encountered attribute error.

    How can I resolve the error ‘no attribute ‘readNetFromCaffe”?

    To address this error, replace cv2.dnn.readNetFromCaffe with cv2.dnn.readNet.

    Where can I find suitable pre-trained weights and configurations for my task?

    Numerous online sources offer pre-trained models compatible with OpenCV’s DNN module. Look into repositories or official websites of specific models for relevant files.

    Can I utilize different deep learning architectures apart from SSD with OpenCV?

    Certainly! OpenCV supports various deep learning architectures like YOLO, Faster R-CNN, providing flexibility in choosing models based on your needs.

    Is there a difference between readNet and readNetFromCaffe methods?

    The primary distinction lies in compatibility; readNet enables loading models trained through Caffe framework directly without requiring a separate function like readNetFromCaffe.

    How crucial are accurate file paths when loading weights and configurations?

    Correct file paths ensure precise location of necessary files during model initialization by OpenCV, preventing errors related to missing resources.

    Can I fine-tune these pre-trained models according to my dataset?

    Deep learning frameworks such as TensorFlow or PyTorch offer features for fine-tuning existing models on custom datasets tailored to specific tasks beyond what OpenCV provides directly.


    Resolving attribute-related issues like ‘no attribute ‘readNeFronmCAffe” necessitates understanding alternative methods within utilized libraries. Adapting code snippets by replacing deprecated functions with newer alternatives ensures smooth execution of tasks involving complex operations such as Deep Neural Networks.

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