"MXNet" Eighth bomb _ object detection of SSD

Pre-and API introduction Mxnet.metric

From mxnet Import metriccls_metric = metric. Accuracy () Box_metric = metric. MAE () cls_metric.update ([Cls_target], [Class_preds.transpose ((0,2,1))]) box_metric.update ([Box_target], [box_preds * Box_mask]) cls_metric.get () Box_metric.get ()

Gluon.loss.Loss

Class Focalloss (Gluon.loss.Loss):    def __init__ (self, axis=-1, alpha=0.25, gamma=2, Batch_axis=0, **kwargs):        Super (Focalloss, self). __init__ (None, Batch_axis, **kwargs)        self._axis = Axis        Self._alpha = Alpha        self._ Gamma = Gamma    def hybrid_forward (self, f, output, label):        # here ' F ' can is either mx.nd or Mx.sym        # here use F instead of F Orward explicitly specify both for ease of use        # so non-hybrid without this parameter        output = F.softmax (output)        PJ = output.pick (label, Axis=self._axis, Keepdims=true)        loss =-Self._alpha * ((1-PJ) * * self._gamma) * Pj.log ()        return Loss.mean (Axis=self._batch_axi S, exclude=true)

Mxnet.contrib.ndarray.MultiBoxTarget

def training_targets (anchors, class_preds, labels): "" "Get all the border    coordinates to    get all the borders of each category score    real category and corresponding border coordinates    " ""    class_preds = Class_preds.transpose (axes= (0,2,1))    

Mxnet.contrib.ndarray.MultiBoxDetection

"MXNet" Eighth bomb _ object detection of SSD