LightNet: Bringing pjreddie's DarkNet out of the shadows

LightNet provides a simple and efficient Python interface to DarkNet, a neural network library written by Joseph Redmon that's well known for its state-of-the-art object detection models, YOLO and YOLOv2. LightNet's main purpose for now is to power Prodigy's upcoming object detection and image segmentation features. However, it may be useful to anyone interested in the DarkNet library.

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LightNet's features include:

• State-of-the-art object detection: YOLOv2 offers unmatched speed/accuracy trade-offs.
• Easy-to-use via Python: Pass in byte strings, get back numpy arrays with bounding boxes.
• Lightweight and self-contained: No dependency on large frameworks like Tensorflow, PyTorch etc. The DarkNet source is provided in the package.
• Easy to install: Just pip install lightnet and python -m lightnet download yolo.
• Cross-platform: Works on OSX and Linux, on Python 2.7, 3.5 and 3.6.
• 10x faster on CPU: Uses BLAS for its matrix multiplications routines.
• Not named DarkNet: Avoids some potentially awkward misunderstandings.

🌓 Installation

Operating system	macOS / OS X, Linux (Windows coming soon)
Python version	CPython 2.7, 3.5, 3.6. Only 64 bit.
Package managers	pip (source packages only)

LightNet requires an installation of OpenBLAS:

sudo apt-get install libopenblas-dev

LightNet can be installed via pip:

pip install lightnet

Once you've downloaded LightNet, you can install a model using the lightnet download command. This will save the models in the lightnet/data directory. If you've installed LightNet system-wide, make sure to run the command as administrator.

python -m lightnet download tiny-yolo
python -m lightnet download yolo

The following models are currently available via the download command:

yolo.weights	258 MB	Direct download
tiny-yolo.weights	44.9 MB	Direct download

🌓 Usage

An object detection system predicts labelled bounding boxes on an image. The label scheme comes from the training data, so different models will have different label sets. YOLOv2 can detect objects in images of any resolution. Smaller images will be faster to predict, while high resolution images will give you better object detection accuracy.

Images can be loaded by file-path, by JPEG-encoded byte-string, or by numpy array. If passing in a numpy array, it should be of dtype float32, and shape (width, height, colors).

importlightnetmodel=lightnet.load('tiny-yolo')
image=lightnet.Image.from_bytes(open('eagle.jpg', 'rb').read())
boxes=model(image)

METHOD lightnet.load

Load a pre-trained model. If a path is provided, it shoud be a directory containing two files, named {name}.weights and {name}.cfg. If a path is not provided, the built-in data directory is used, which is located within the LightNet package.

model=lightnet.load('tiny-yolo')
model=lightnet.load(path='/path/to/yolo')

Argument	Type	Description
name	unicode	Name of the model located in the data directory, e.g. tiny-yolo.
path	unicode	Optional path to a model data directory.
RETURNS	Network	The loaded model.

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🌓 Network

The neural network object. Wraps DarkNet's network struct.

CLASSMETHOD Network.load

Load a pre-trained model. Identical to lightnet.load().

METHOD Network.__call__

Detect bounding boxes given an Image object. The bounding boxes are provided as a list, with each entry (class_id, class_name, prob, [(x, y, width, height)]), where `x` and y` are the pixel coordinates of the center of the centre of the box, and width and height describe its dimensions. class_id is the integer index of the object type, class_name is a string with the object type, and prob is a float indicating the detection score. The thresh parameter controls the prediction threshold. Objects with a detection probability above thresh are returned. We don't know what hier_thresh or nms do.

boxes=model(image, thresh=0.5, hier_thresh=0.5, nms=0.45)

Argument	Type	Description
image	Image	The image to process.
thresh	float	Prediction threshold.
hier_thresh	float
path	unicode	Optional path to a model data directory.
RETURNS	list	The bounding boxes, as (class_id, class_name, prob, xywh) tuples.

METHOD Network.update

Update the model, on a batch of examples. The images should be provided as a list of Image objects. The box_labels should be a list of BoxLabel objects. Returns a float, indicating how much the models prediction differed from the provided true labels.

loss=model.update([image1, image2], [box_labels1, box_labels2])

Argument	Type	Description
images	list	List of Image objects.
box_labels	list	List of BoxLabel objects.
RETURNS	float	The loss indicating how much the prediction differed from the provided labels.

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🌓 Image

Data container for a single image. Wraps DarkNet's image struct.

METHOD Image.__init__

Create an image. data should be a numpy array of dtype float32, and shape (width, height, colors).

image=Image(data)

Argument	Type	Description
data	numpy array	The image data
RETURNS	Image	The newly constructed object.

CLASSMETHOD Image.blank

Create a blank image, of specified dimensions.

image=Image.blank(width, height, colors)

Argument	Type	Description
width	int	The image width, in pixels.
height	int	The image height, in pixels.
colors	int	The number of color channels (usually 3).
RETURNS	Image	The newly constructed object.

CLASSMETHOD Image.load

Load an image from a path to a jpeg file, of the specified dimensions.

image=Image.load(path, width, height, colors)

Argument	Type	Description
path	unicode	The path to the image file.
width	int	The image width, in pixels.
height	int	The image height, in pixels.
colors	int	The number of color channels (usually 3).
RETURNS	Image	The newly constructed object.

CLASSMETHOD Image.from_bytes

Read an image from a byte-string, which should be the contents of a jpeg file.

image=Image.from_bytes(bytes_data)

Argument	Type	Description
bytes_data	bytes	The image contents.
RETURNS	Image	The newly constructed object.

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🌓 BoxLabels

Data container for labelled bounding boxes for a single image. Wraps an array of DarkNet's box_label struct.

METHOD BoxLabels.__init__

Labelled box annotations for a single image, used to update the model. ids should be a 1d numpy array of dtype int32, indicating the correct class IDs of the objects. boxes should be a 2d array of dtype float32, and shape (len(ids), 4). The 4 columns of the boxes should provide the relativex, y, width, height of the bounding box, where x and y are the coordinates of the centre, relative to the image size, and width and height are the relative dimensions of the box.

box_labels=BoxLabels(ids, boxes)

Argument	Type	Description
ids	numpy array	The class IDs of the objects.
boxes	numpy array	The boxes providing the relative x, y, width, height of the bounding box.
RETURNS	BoxLabels	The newly constructed object.

CLASSMETHOD BoxLabels.load

Load annotations for a single image from a text file. Each box should be described on a single line, in the format class_id x y width height.

box_labels=BoxLabels.load(path)

Argument	Type	Description
path	unicode	The path to load from.
RETURNS	BoxLabels	The newly constructed object.