Tecno camon 18 Premiere
Video Microscope
Regular themes we generate by training deep learning models have constraints — usually seen during training — which are “output values” we could use, often giving us a quick estimate of the end result. At the beginning of the machine learning cycle, in order to label the images, we are given a couple of inputs, which we generally use as “trainable data”.
Sometimes though, we come across a high-value trainable dataset which we do not yet have the training data set for, which are called “off-range patterns”. At the end of the training training, we receive these patterns, which could be magnified and better represented as weights towards the new model(s) that will be built upon. Typically, such off-range patterns are produced by different features such as color, texture, density, etc.
For our purposes, it is preferable to focus on the off-range patterns being produced by our videos, which will then be fed into the trained model(s) which we will apply those features to produce more accurate predictions for the next task.
Our original Input Images
Those are some key visualizations, to better see the impact the Camon 18 10 megapixel device has on our videos:
Hudson DCF-M2000 Series Camon 18 prototype film
The image results are similar to the ones above — with more prominent off-range patterns being produced — which might lead us to believe the Camon 18 prototype will produce better results for better resolution of the video output, but don’t be deceived by the fact that the sample size is small: it’s not correct to say that for 30 seconds the Camon 18 prototype produced some surprising results, it won’t be able to produce as many such results later on in the test.
Also note — that this series of three videos are all different sizes and all different people. It’s likely that future models will produce similar results across different scenarios. It’s impossible to know now, just how much the detected patterns would change the results produced by the (P3-20) model.
This test shows that the Camon 18 produces even more than the more expensive 20–30 Mb model. Seeing as we had the most expensive model in hand, it shows that we have a very useful model that will produce good results for our sports videos. Even though the results from our budget 3×30 Mb Camon 18 have been very close to the optimal, we also have to consider the difference in data volume between the models — we are taking the sample size for the original Camon 18 in this test, (30 seconds of video, at 400 x 400×216), while taking that information into account. Our 3X30 Mb Camon 18 has only been burning and sorting video, but our 20–30 Mb model uses a lot of time watching multiple frames, which might lead to less successful match-ups. But we will get back to this topic soon (maybe we want a 100 Meg)
Also, when we go back to the original M2520 prototype of the Camon 18, the results are all different, as it says in the image above, which lead us to believe that the Camon 18 is not only the most efficient and fastest home cinema camcorder in terms of its speed, but also the best performer.
Interestingly, in these three videos, the Camon 18 produced some good results — even while collecting 20–30 Mb from the original camcorder. We need more test shots with those CAMON 18 to get really clear evaluations as well as results.
It’s clear that there’s a lot of potential yet…
Thanks for reading the introduction to an experiment study. If you liked what you read and would like to follow my next stories, please let me know at hello@deeplearningstudy.com. Please also follow my Medium channel if you don’t already.
Dr. Clare Valance
Research Director
Deep Learning Study
Team Founded: Novanara
About Me:
Clare Valance is an MIT graduate and professional researcher from 2015-2017
Originally published at deeplearningstudy.com on November 25, 2021.
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