如何解释YOLOv4输出

如何解决如何解释YOLOv4输出

我正在尝试使用YOLOv4上的TensorFlowLite来解释iOS的输出。我已经阅读了一些关于输出张量中的cells and anchors概念的知识，并且我想为其实现解析器。我的模型的输出包含2个张量：

float32[1,13,255]
float32[1,26,255]

第一件事我想知道第二输出是什么？从我阅读的内容来看，第一个应该包含所有信息。第二个只是更详细的结果还是其他？

好，但是让我们从第一个输出开始。我编写了一个简单的函数，该函数应该提取所有单元格，然后提取这些单元格中的所有锚点数据。看起来是这样：

        let output0 = try localModel.output(at: 0)
        guard let output0Floats = [Float](bytes: output0.data) else { return nil }
        
        let numberOfCells = 13
        let numberOfAnchors = 3
        let numberOfClasses = 80
        let anchorSize = (numberOfClasses + 5)
        
        func cellAt(x: Int,y: Int) -> [Float] {
            let cellSize = anchorSize * numberOfAnchors
            let position = (y * numberOfCells + x) * cellSize
            return [Float](output0Floats[position..<position + cellSize])
        }
        
        func anchors(in cell: [Float]) -> [[Float]] {
            (0..<numberOfAnchors).map { [Float](cell[$0 * anchorSize..<$0 * anchorSize + anchorSize]) }
        }
        
        for y in 0..<numberOfCells {
            for x in 0..<numberOfCells {
                let cell = cellAt(x: x,y: y)
                print("Cell: \(x),\(y) contains anchors:")
                print(anchors(in: cell))
            }
        }

...

private extension Array {

    init?(bytes: Data) {
        guard bytes.count % MemoryLayout<Element>.stride == 0 else { return nil }
        self = bytes.withUnsafeBytes { .init($0.bindMemory(to: Element.self)) }
    }

}

这是我为单个单元格得到的示例结果：

Cell: 7,12 contains anchors:
[[0.693655,-1.1966848,-0.007975042,-0.3327814,-9.583811,0.3976263,-6.0192285,-6.329881,-5.8644676,-10.2914715,-9.632221,-8.071436,-6.399925,-5.240812,-8.791572,-5.6437893,-9.8603115,-10.492198,-1.9372412,-7.0640965,-2.6936512,-5.112247,-7.131972,-7.1825066,-7.4413238,-10.401382,-7.5643044,-8.608834,-8.239082,-6.799241,-8.035741,-5.7502255,-8.881622,-7.3571744,-9.315964,-7.925786,-7.7857537,-4.8930154,-8.529579,-7.633353,-8.817726,-7.47082,-8.291334,-4.683982,-4.170734,-6.193165,-7.8437185,-9.854808,-9.490823,-8.272433,-8.434413,-7.765057,-7.149798,-11.194118,-6.5116143,-11.112444,-9.999684,-10.689343,-9.942104,-9.520727,-7.440444,-2.531265,-3.7234814,-7.5839844,-4.550161,-3.031804,-4.616852,-8.832014,-6.0279136,-9.482858,-6.750441,-8.450063,-10.222086,-7.6301804,-7.559189,-10.234117,-6.999834,-7.1350074,-5.308107,-6.2450233,-8.8833885,-9.381562,-3.8812854,-8.868278,-9.988986],[0.4351927,-1.3958519,0.46428338,-0.39240548,-8.170114,0.7084342,-7.709829,-5.9856057,-6.808081,-10.644019,-9.912677,-7.3293757,-7.548369,-5.533275,-10.072926,-7.316476,-9.945337,-11.118561,-3.2463353,-10.561513,-5.067392,-7.312641,-8.729989,-9.5539055,-7.58917,-9.886164,-6.5404315,-8.553915,-9.023286,-9.580754,-6.7592535,-8.380334,-8.182065,-7.2239976,-9.276712,-7.5086412,-7.2454534,-7.139829,-8.614485,-7.8158274,-9.850543,-9.123642,-6.8081083,-6.936388,-7.997142,-8.845028,-11.322939,-10.713314,-9.629859,-10.820017,-10.480835,-9.071951,-7.9244685,-12.562474,-7.1654305,-13.456438,-10.116255,-12.255847,-11.530319,-10.3949375,-10.665162,-5.6975913,-4.050809,-10.665826,-2.638548,-3.5531735,-7.0320325,-10.047072,-7.678191,-10.290669,-7.438999,-7.531754,-9.817409,-8.428637,-9.502961,-10.955662,-8.6340065,-5.0168147,-8.593948,-9.412493,-10.816083,-10.903126,-8.81499,-10.449745,-9.069517],[0.025469145,-1.7808459,-0.18256505,-0.70104045,-10.450736,-0.67288893,-5.771856,-5.448979,-6.4159226,-8.777289,-7.960696,-5.3555217,-4.798117,-2.8378687,-7.9489646,-8.255625,-8.968552,-8.036578,-2.46956,-8.458385,-4.8979797,-6.5746903,-7.2408285,-8.574903,-6.8356185,-6.4320874,-6.037178,-7.56021,-7.275848,-8.808907,-3.9019513,-8.835796,-6.360187,-6.5461373,-7.1117754,-6.6027184,-7.280362,-7.1671834,-7.292713,-7.1488175,-7.1398635,-8.180893,-5.797153,-6.3417816,-6.9332256,-8.371075,-9.2042055,-8.602686,-8.072069,-8.1690035,-8.0164175,-6.61691,-6.3536263,-9.318304,-4.5542707,-10.049933,-7.8087454,-9.497473,-9.07455,-8.406244,-7.078502,-5.5775504,-2.3586287,-8.409487,-1.6716739,-3.8225765,-6.9020715,-6.6682305,-5.784493,-8.40492,-7.2747784,-6.392035,-6.4958863,-7.629692,-7.4995623,-8.4432125,-6.7565637,-3.113231,-7.3596015,-8.573539,-8.829562,-8.523581,-8.571439,-8.087017,-7.958835]]

因此单个锚点如下所示：

[0.693655,-9.988986]

现在我不明白这些数字。根据我的阅读，前5个数字应该是：

信心，BBoxX，BBoxY，BBoxWidth，BBoxHeight 其余的值是labelMap中每个类的概率。

但是这些数字对我来说似乎完全不正确。置信度不应该介于0和1之间吗？概率不应该在0到1之间吗？获得这些结果会导致我做错什么？我在解析这些结果之前使用的代码已经与其他类型的tflite文件进行了很好的测试，应该没问题。这可能是由于在输入像素缓冲区准备中使用了错误的imageMean和imageStd吗？我不确定要使用哪个值来构建此模型，因此Im对这两个值都使用了127.5。

如何解释YOLOv4输出

如何解决如何解释YOLOv4输出

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