The therapeutic attack on MS has been significantly enriched in recent years and now permits for the first time a rational algorithm for applying first-line and second-line medications 4. Investigating children with MS, a minority whose existence is making an outsized contribution to research, has been particularly effective for validating the environmental risk factors for disease occurrence. Environmental factors that predispose to MS, mainly low vitamin D and infection with Epstein-Barr virus, have also been elucidated 2, 3. These studies show convincingly that immune factors are of paramount importance and that MS shares genetic characteristics of other organ-specific inflammatory disorders such as rheumatoid arthritis and inflammatory bowel disease 1. Recent years have seen progress on all fronts, with the genetic traits underlying MS susceptibility largely established at the genome-wide association study (GWAS) level. The disorder has for many years been explained vaguely by a coincidence of environmental factors and unexplained influences occurring against a genetically susceptible background, and it lacks highly effective treatments. As a spur to research efforts, the disease mainly begins in a person's late 20s to early 30s, runs a chronic and often debilitating course, and carries a large burden of suffering and expense for approximately 2.5 million affected worldwide.
However, personal diaries and historical works push back descriptions of the disease at least 50, and perhaps 500, years earlier (lucidly summarized at ).įrom the first, MS gripped investigators, and it continues to do so. Pathological and clinical phenomena of MS were described in the first half of the nineteenth century by Carswell, Cruveilhier, von Frerichs and Rokitansky, and the crucial step of establishing the clinical-pathological relationships of MS, thereby characterizing the disease as a distinct entity, is attributed to Charcot in the latter half of the nineteenth century. Hopefully you see something like this: data/dog.jpg: Predicted in 0.160994 seconds.MS is a sporadic inflammatory demyelinating disease of the human CNS. darknet classify cfg/tiny.cfg tiny.weights data/dog.jpg
#Lil nn models how to#
Here's how to use it in Darknet (and also how to install Darknet): git clone
The real winner here is clearly the Darknet reference model but if you insist on wanting a small model, use Tiny Darknet. Alexnet was a great first pass at classification but we shouldn't be stuck back in the days when networks this bad are also this slow!īut anyway, people are super into SqueezeNet so if you really insist on small networks, use this: Tiny Darknet Model So what about SqueezeNet? Sure the weights are only 4.8 MB but a forward pass is still 2.2 billion operations. Darknet is 2.9 times faster and it's small and it's 4% more accurate. When most high quality images are 10MB or more why do we care if our models are 5 MB or 50 MB? If you want a small model that's actually FAST, why not check out the Darknet reference network? It's only 28 MB but more importantly, it's only 800 million floating point operations. SqueezeNet is cool but it's JUST optimizing for parameter count.
I've heard a lot of people talking about SqueezeNet.
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