![]() ![]() ![]() darknet classify cfg/tiny.cfg tiny.weights data/dog.jpg 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.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |