YOLO¶

Object Detection Link¶

class chainercv.experimental.links.model.yolo.YOLOv2Tiny(n_fg_class=None, pretrained_model=None)[source]¶

YOLOv2 tiny.

This is a model of YOLOv2 tiny a.k.a. Tiny YOLO. This model uses DarknetExtractor as its feature extractor.

Parameters

n_fg_class (int) – The number of classes excluding the background.
pretrained_model (string) –
The weight file to be loaded. This can take 'voc0712', filepath or None. The default value is None.
- 'voc0712': Load weights trained on trainval split of PASCAL VOC 2007 and 2012. The weight file is downloaded and cached automatically. n_fg_class must be 20 or None. These weights were converted from the darknet model provided by the original implementation. The conversion code is chainercv/examples/yolo/darknet2npz.py.
- filepath: A path of npz file. In this case, n_fg_class must be specified properly.
- None: Do not load weights.

class chainercv.experimental.links.model.yolo.DarknetExtractor[source]¶

A Darknet based feature extractor for YOLOv2Tiny.

This is a feature extractor for YOLOv2Tiny

forward(x)[source]¶

Compute a feature map from a batch of images.

Parameters: x (ndarray) – An array holding a batch of images. The images should be resized to \(416\times 416\).
Returns
Return type: Variable