# Tiny Darknet

I've heard a lot of people talking about SqueezeNet.

SqueezeNet is cool but it's JUST optimizing for parameter count. 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. The original Alexnet is 2.3 billion. Darknet is 2.9 times faster and it's small and it's 4% more accurate.

So what about SqueezeNet? Sure the weights are only 4.8 MB but a forward pass is still 2.2 billion operations. 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!

But anyway, people are super into SqueezeNet so if you really insist on small networks, use this:

## Tiny Darknet

Model | Top-1 | Top-5 | Ops | Size |
---|---|---|---|---|

AlexNet | 57.0 | 80.3 | 2.27 Bn | 238 MB |

Darknet Reference | 61.1 | 83.0 | 0.81 Bn | 28 MB |

SqueezeNet | 57.5 | 80.3 | 2.17 Bn | 4.8 MB |

Tiny Darknet | 58.7 | 81.7 | 0.98 Bn | 4.0 MB |

The real winner here is clearly the Darknet reference model but if you *insist* on wanting a small model, use Tiny Darknet. Or train your own, it should be easy!

Here's how to use it in Darknet (and also how to install Darknet):

```
git clone https://github.com/pjreddie/darknet
cd darknet
make
wget https://pjreddie.com/media/files/tiny.weights
./darknet classify cfg/tiny.cfg tiny.weights data/dog.jpg
```

Hopefully you see something like this:

```
data/dog.jpg: Predicted in 0.160994 seconds.
malamute: 0.167168
Eskimo dog: 0.065828
dogsled: 0.063020
standard schnauzer: 0.051153
Siberian husky: 0.037506
```

Here's the config file: tiny.cfg

The model is just some 3x3 and 1x1 convolutional layers:

layer filters size input output 0 conv 16 3 x 3 / 1 224 x 224 x 3 -> 224 x 224 x 16 1 max 2 x 2 / 2 224 x 224 x 16 -> 112 x 112 x 16 2 conv 32 3 x 3 / 1 112 x 112 x 16 -> 112 x 112 x 32 3 max 2 x 2 / 2 112 x 112 x 32 -> 56 x 56 x 32 4 conv 16 1 x 1 / 1 56 x 56 x 32 -> 56 x 56 x 16 5 conv 128 3 x 3 / 1 56 x 56 x 16 -> 56 x 56 x 128 6 conv 16 1 x 1 / 1 56 x 56 x 128 -> 56 x 56 x 16 7 conv 128 3 x 3 / 1 56 x 56 x 16 -> 56 x 56 x 128 8 max 2 x 2 / 2 56 x 56 x 128 -> 28 x 28 x 128 9 conv 32 1 x 1 / 1 28 x 28 x 128 -> 28 x 28 x 32 10 conv 256 3 x 3 / 1 28 x 28 x 32 -> 28 x 28 x 256 11 conv 32 1 x 1 / 1 28 x 28 x 256 -> 28 x 28 x 32 12 conv 256 3 x 3 / 1 28 x 28 x 32 -> 28 x 28 x 256 13 max 2 x 2 / 2 28 x 28 x 256 -> 14 x 14 x 256 14 conv 64 1 x 1 / 1 14 x 14 x 256 -> 14 x 14 x 64 15 conv 512 3 x 3 / 1 14 x 14 x 64 -> 14 x 14 x 512 16 conv 64 1 x 1 / 1 14 x 14 x 512 -> 14 x 14 x 64 17 conv 512 3 x 3 / 1 14 x 14 x 64 -> 14 x 14 x 512 18 conv 128 1 x 1 / 1 14 x 14 x 512 -> 14 x 14 x 128 19 conv 1000 1 x 1 / 1 14 x 14 x 128 -> 14 x 14 x1000 20 avg 14 x 14 x1000 -> 1000 21 softmax 1000 22 cost 1000