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(this article is part of TensorFlow Object_detection API framework, which is used to train your own model anchor box.) Many object detection models use anchor boxes as the area sampling strategy, so during training, the model learns to match one of several predefined anchor boxes with the ground live boundary box. In order to optimize the accur ...

General introduction The project is mainly based on github project Introduce how to access TIPC and how to support serving. For more information about the original project, please see readme Understand MD. In addition, you can also refer to the official TIPC and Serving To connect your own model to TIPC and serve support. TIPC basic chain &c ...

5.1 Classification learning The qualitative output of Classification problem is Classification, or discrete variable prediction. Regression problem, the quantitative output is regression, or continuous variable prediction. """ Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly. """ f ...

Python OpenCv realizes real-time face recognition and face ranging prepare Before the development of facial ranging, first install four libraries in your Python, namely cvzone library, mediapipe library, tensorflow library and tensorflow GPU library. It is faster to install cvzone library and mediapipe library. You can install them quickly by ...

In the neural network optimization algorithm, the most commonly used method is the back propagation algorithm, and its work flow is as follows: As shown in the figure, the back propagation algorithm implements an iterative process. At the beginning of each iteration, a part of training data is selected, which is called a batch. Then, the ...

Learning latent variables using self encoder Because there are many redundancies in the high-dimensional input space, it can be compressed into some low-dimensional variables. The self encoder was first introduced by Geoffrey Hinton et al. In the 1980s. Similar to the technology used to reduce the input dimension in traditional machine lea ...

1, Some tools 1. Three features of local printout non sequential sequence data_ Method of column converted value Applicable to tensorflow1 x import tensorflow as tf from tensorflow.python.feature_column import feature_column_v2 as fc_v2 from tensorflow.python.feature_column import feature_column as fc # Note: only mode 2 will check whether t ...

reference resources Training image classifier introduce The image classification model has millions of parameters. Training from scratch requires a lot of labeled training data and a lot of computing power. Transfer learning is a skill that greatly simplifies the process by adopting a model that has been trained on related tasks and reusi ...

TensorFlow 2's default Eagle execution mode brings us flexibility and easy debugging. However, in order to pursue faster speed and higher performance, we still want to use tensorflow 1 The default Graph Execution mode in X. At this point, TensorFlow 2 provides us with TF Function module, combined with AutoGraph mechanism, makes us only need to ...

Summary of face alignment and face key point detection based on Tensorflow framework with code (continuously updated)    Summary of face detection based on Tensorflow framework with code (continuously updated)    Summary of face matching based on Tensorflow framework with code (continuously updated)    recently used the open sourc ...