nvme ssd vs ssd

Transferred from: http://lanbing510.info/2017/08/28/YOLO-SSD.html Prior to the emergence of deep learning, the traditional target detection method is probably divided into regional selection (sliding window), feature extraction (SIFT, hog, etc.), classifier (SVM, adaboost, etc.) three parts, the main problems have two aspects: on the one hand, sliding window selection strategy is not targeted, time complexity, window redundancy On the other hand, the characteristics of manual design are poor. S

In a variety of test mix scenarios, the combination of 10 (combination 10:SSD * 2, RAID 0, Xfs,wb,nobarrier,noop) has the highest overall performance, so it is the benchmark, and the comparison of other schemes, the following table is the comparison of the combinations and combinations 10:650) this.width=650; "Src=" http://dp.imysql.com:8080/files/upload_yejr_imysql/SAS_VS_SSD_MySQL_OLTP%E5%AF%B9%E6% Af%94%e6%b5%8b%e8%af%95%e8%a1%a8-20120907.png "styl

does not mean that the hard disk can not be used, but performance will be reduced, there is noise, such as driving a car, the mechanical structure will appear aging, so need maintenance, but mechanical hard disk use for 5-10 years completely no problem, or even more. Solid-state hard disk life standard is generally written or written capacity, the calculation of capacity units are not intuitive, including 4 K, 8K of random write capacity, usually in TB, PB units to calculate, the general profe

Pre-and API introduction Mxnet.metricFrom 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.LossClass 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 =

SSD predictive bbox is essentially the same as RPN, except that it predicts on multiple layers to better detect objects on multiple scales. predicting bbox on multiple layers The original SSD300 is predicted on the following layer: Conv4 ==> x conv7 ==> x conv8 ==> 5 x 5 conv9 ==> 3 3 CONV11 ==> 1 x 1 The number that follows is the size of the feature map for this layer output.SSD512: Conv4 ==> x conv7 ==> x conv8