tensorflow object detection example

1. Migration Learning Migration learning is also known as supervised pre-training (supervised pre-training), which we often call migration learning. For example, you already have a bunch of picture data labeled for the age of the face, trained a CNN to recognize the age of the human face. Then when you meet a new project task is: Face sex recognition, then you can use the trained age to identify the CNN model, remove the last layer, and then the other

when the finger is moving. _ontouchstart (e) { ... if (E.touches.length > 1) { This.touchvector = { X:point2.pagex-this.startx, Y:point2.pagey-this.starty }; } ... } _ontouchmove (e) { ... if (this.touchvector) { Let vector = { X:e.touches[1].pagex-e.touches[0].pagex, Y:e.touches[1].pagey-e.touches[0].pagey }; Let angle = This._getrotateangle (vector,this.touchvector); This._emitevent (' Onrotate ', { Angle }); this.touchvector.x = vector.x; This.touchvector.y = VECTOR.Y;

function of p, and the P is extended to the real field, and the most valued point isThe function rises first and then drops, the limit is 0. Suppose that when P=1 is the desired distance of the mean, there will be a lot of p, which can make the distance increase. Suppose that, if, can be rolled out, This indicates that one of its selected features represents more than half of the patches in the image (this sentence I understand is, because that is the probability of selecting/generating feature

original RCNN, accurate rate is not badSummaryStrictly speaking spp-net is not for the detection model, but spp-net for rcnn evolution to fast-rcnn played a great role, it is worth reading. Spp-net idea is very interesting, SPP (Spatial Pyramid Pooling) is an improvement of the network structure, probably because it is Chinese writing paper, feel very good reading, gold content personal feeling no rcnn or DPM paper High, but the experiment is very ri

Haven't written a blog for a long time. In fact, there is nothing to write yourself ... The level is limited, eh .... The robot has recently been engaged in kinematics, so the depth vision is neglected here. However, recently saw a previous article feel very interesting, so want to take out and share with you. The title of the article is titled "Sliding Shapes for 3D object detection in Depth Images", whi

unlike rcnn each candidate area to the depth of the network feature, but the entire map to mention a feature, and then map the candidate frame to the CONV5, Because the size of the candidate box is different, the mapping to the CONV5 is still different, so the SPP layer will need to be extracted to the same dimension of the characteristics, and then classification and regression, the following ideas and methods are consistent with RCNN. In fact, this is a lot faster than the original, because b

PHP: Array data type instance details in PHP, there are a series of functions that detect data types, which can be used to detect different types of data to determine if they belong to the meter data type, and return true if they match, otherwise false. The functions of the PHP detection data type are the following table Function Detection type Examp