deep-license-plate-recognition VS tensorflow

One of the plate recognizers I used identified the plate as NAK N97. However the license plate search turned up nothing. https://platerecognizer.com/ I cropped the picture of the car in question and uploaded it to the plate recognizer site. It labeled the make and model of the car as well as revealing the plate number it thought it saw.

Du kan prøve at uploade det her: https://platerecognizer.com/

if anyone knows an up-to-date license plate reader version. https://github.com/openalpr/openalpr is outated, doesn't read different angles natively, traning tools are also outated etc but anyone knows an open source version that as well as say https://platerecognizer.com/ or even https://www.openalpr.com/ please let me know.

If you can get a screenshot with the license plate, try uploading it to a service like https://platerecognizer.com

You can't even escape it in real-life! Proximity marketing with Bluetooth beacons. Wifi-tracking. People-counters. Gaze-tracking. Prism (old tech! lol). License-plate tracking. Hundreds of apps have backdoors in them to record your geolocation data. Car-tapping. Telematics.

Yes, https://platerecognizer.com/

# L2-normalize the encoding tensors image_encoding = tf.math.l2_normalize(image_encoding, axis=1) audio_encoding = tf.math.l2_normalize(audio_encoding, axis=1) # Find euclidean distance between image_encoding and audio_encoding # Essentially trying to detect if the face is saying the audio # Will return nan without the 1e-12 offset due to https://github.com/tensorflow/tensorflow/issues/12071 d = tf.norm((image_encoding - audio_encoding) + 1e-12, ord='euclidean', axis=1, keepdims=True) discriminator = keras.Model(inputs=[image_input, audio_input], outputs=[d], name="discriminator")

[3]: https://github.com/tensorflow/tensorflow/graphs/contributors

To get up to speed with TensorFlow, check their quickstart Support TensorFlow on GitHub ⭐

Explore here.

Part of the major userbase pull in GitHub revolves around hosting a considerable number of popular projects including Angular, React, Kubernetes, cpython, Ruby, tensorflow, and well even the software that powers this site Forem.

Right but that's not an inherent GPU determinism issue. It's a software issue.

https://github.com/tensorflow/tensorflow/issues/3103#issueco... is correct that it's not necessary, it's a choice.

Your line of reasoning appears to be "GPUs are inherently non-deterministic don't be quick to judge someone's code" which as far as I can tell is dead wrong.

Admittedly there are some cases and instructions that may result in non-determinism but they are inherently necessary. The author should thinking carefully before introducing non-determinism. There are many scenarios where it is irrelevant, but ultimately the issue we are discussing here isn't the GPU's fault.

and things like y = layers.ELU()(y) work as expected. I wanted to see a list of the available layers so I went to the Tensorflow GitHub repository and to the keras directory. There's a warning in that directory that says:

Exactly what language do you think TensorFlow is written in? :)