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|
Image Recognition Engine
========================
This is sample HTTP service for object detection using the
[YOLO](https://pjreddie.com/darknet/yolo/) model.
It comes with simple Web interface.
Requirements
============
IRE is written in Haskell with [GHC](http://www.haskell.org/ghc/).
All required Haskell libraries are listed in [ire.cabal](ire.cabal).
Use [cabal-install](http://www.haskell.org/haskellwiki/Cabal-Install)
to fetch and build all pre-requisites automatically.
It also uses modified [Darknet library](https://github.com/ip1981/darknet),
which you have to build and install separately.
Installation
============
$ git clone https://github.com/ip1981/ire.git
$ cd ire
$ cabal install --dependencies-only
$ cabal install --ghc-options="-optl=-Wl,-rpath,$darknet/lib" --extra-include-dirs "$darknet/include" --extra-lib-dirs "$darknet/lib"
Usage
=====
Type `ire --help` to see usage summary:
Usage:
ire [options]
Options:
-c, --config=FILE Configuration file [default: ire.yml]
-r, --rootdir=DIR Web root directory with static files [default: <cabal datadir>]
-h, --help Show this message
Note:
The default configuration file is loaded if found,
otherwise default built-in settings are used.
Configuration
=============
You have to provide network configuration file, weights file and class names.
See [example configuration file](./ire.example.yml) and Darknet source code.
Running
=======
```
$ ire
layer filters size input output
0 conv 32 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 32
1 max 2 x 2 / 2 416 x 416 x 32 -> 208 x 208 x 32
2 conv 64 3 x 3 / 1 208 x 208 x 32 -> 208 x 208 x 64
3 max 2 x 2 / 2 208 x 208 x 64 -> 104 x 104 x 64
4 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128
5 conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64
6 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128
7 max 2 x 2 / 2 104 x 104 x 128 -> 52 x 52 x 128
8 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
9 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
10 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
11 max 2 x 2 / 2 52 x 52 x 256 -> 26 x 26 x 256
12 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
13 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
14 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
15 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
16 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
17 max 2 x 2 / 2 26 x 26 x 512 -> 13 x 13 x 512
18 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
19 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512
20 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
21 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512
22 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
23 conv 1024 3 x 3 / 1 13 x 13 x1024 -> 13 x 13 x1024
24 conv 1024 3 x 3 / 1 13 x 13 x1024 -> 13 x 13 x1024
25 route 16
26 reorg / 2 26 x 26 x 512 -> 13 x 13 x2048
27 route 26 24
28 conv 1024 3 x 3 / 1 13 x 13 x3072 -> 13 x 13 x1024
29 conv 425 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 425
30 detection
Loading weights from yolo.weights...Done!
INFO: Listening on localhost:8080
DEBUG: [Item {itemClass = 0, itemName = "person", itemConfidence = 0.8008945, itemBox = (0.3574012,0.42719698,0.6506731,0.24371791)},Item {itemClass = 16, itemName = "dog", itemConfidence = 0.6505284, itemBox = (0.6375929,0.5701367,0.21318695,0.15970974)}]
...
```
|
