In machine learning applications, classification algorithms are used to get a prediction on the data stream in order to label objects for further analysis. In such applications, not only the accuracy of classification algorithms is important but also the sensitivity or true positive rate (detection/recognition rate) of the algorithms. An example of a data imbalance in the training phase of a machine learning model can show the importance of both accuracy and detection rate. An arbitrary class ( e.g. car) with a large number of samples will give good accuracy because a classifier has seen many examples of this class…
Tensorflow | allow_growth| cuDNN failed to initialize| Image classification
We get this error for image classification using TensorFlow on Windows PC with Nvidia . The solution led us to explore some options regarding GPU memory utilization.
Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[node sequential_4/conv2d_12/Relu (defined at <ipython-input-98–471bb77471a2>:2) ]] [Op:__inference_train_function_3889]
The TensorFlow by default allocates full GPU memory even for a small network. …
How to transfer saved passwords from One Google Account to Another
Google has become an integrated part of our digital life. If you are using google chrome functionality of saving password and feels at some point to transfer all saved passwords to another account, follow this step by step tutorial.
In the address bar, write
Developing OpenCV based vision applications with QT open up possibilities of different powerful features such as QT Signals & Slots  and Qthread .
In the given example [ link ], you will see
1-Create your QObjects,
2-connect your signals,
3-create your QThread,
4-move your QObjects to the QThread and
5-start the thread.
Q_DECLARE_METATYPat the beginning of the constructor. …
Often, there are requirements to get multiple data streams with GStreamer pipelines. If there are not handled properly, one could expect a blocking phenomenon as one stream is continuously streaming and not letting other pipelines to get the streams. One can handle them using thread but it could be overwhelming for some developers. In this tutorial, we are using simple QT concurrent thread to handle multiple streams in a non-blocking way. In addition to this, we will show a step-by-step procedure to build google test from source using CMake and include it in the QT project. …
Edge AI together with Cloud computing is paving the way for the new digital era thanks to recent developments in hardware platforms, hardware accelerators, machine learning algorithms for edge devices; cloud computing for offline training, data storage and data management. Not only existing applications are foreseeing the benefits of utilizing the developments in this area but also new kinds of opportunities are emerging in different fields e.g customer insights, check brand affinity, monitor internet content, fraud detection, surveillance, autonomous driving, and predictive maintenance.
Traditional system-level architectures employ Edge and cloud computing as separate entities because of a number of challenges…
A good leader creates a conducive work environment, gives constructive feedback to colleagues, and leads with purpose. A good leader always remains positive and transmits positive energy. A good leader changes between different states of mind easily and releases negative energy before talking. First, we go through how a leader can create a conducive work environment by engaging his colleagues to give them the best employee experience in the organization.
Edge AI has gained momentum after some level of maturity of the AI training frameworks; Tensorflow, Pytorch, Caffe, Keras, OpenVINO, etc. However, a complete tool-chain, from data harvesting to model deployment and inference is still not clear as the work is still in the research phase and developments are very rapid. This, however, has not hampered the development of some exciting solutions in the area. Examples include object recognition from computer vision and speech recognition from a natural language processing perspective.
Many of the existing AI solutions have a cloud computing or storage as an essential component of the architecture…
Imran is a computer vision and AI enthusiast with a PhD in computer vision. Imran loves to share his experience with self-improvement and technology.