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YOLOv5算法详解

青葱年少

内容

1、需求解读

YOLOV4出现之后不久，YOLOv5横空出世。YOLOv5在YOLOv4算法的基础上做了进一步的改进，检测性能得到进一步的提升。虽然YOLOv5算法并没有与YOLOv4算法进行性能比较与分析，但是YOLOv5在COCO数据集上面的测试效果还是挺不错的。大家对YOLOv5算法的创新性半信半疑，有的人对其持肯定态度，有的人对其持否定态度。在我看来，YOLOv5检测算法中还是存在很多可以学习的地方，虽然这些改进思路看来比较简单或者创新点不足，但是它们确定可以提升检测算法的性能。其实工业界往往更喜欢使用这些方法，而不是利用一个超级复杂的算法来获得较高的检测精度。本文将对YOLOv5检测算法中提出的改进思路进行详细的解说，大家可以尝试者将这些改进思路应用到其它的目标检测算法中。

2、YOLOv5算法简介

YOLOv5是一种单阶段目标检测算法，该算法在YOLOv4的基础上添加了一些新的改进思路，使其速度与精度都得到了极大的性能提升。主要的改进思路如下所示：

3、YOLOv5算法详解

3.1 YOLOv5网络架构

上图展示了YOLOv5目标检测算法的整体框图。对于一个目标检测算法而言，我们通常可以将其划分为4个通用的模块，具体包括：输入端、基准网络、Neck网络与Head输出端，对应于上图中的4个红色模块。YOLOv5算法具有4个版本，具体包括：YOLOv5s、YOLOv5m、YOLOv5l、YOLOv5x四种，本文重点讲解YOLOv5s，其它的版本都在该版本的基础上对网络进行加深与加宽。

3.2 YOLOv5实现细节详解

3.2.1 YOLOv5基础组件

3.2.2 输入端细节详解

3.2.3 基准网络细节详解

3.2.4 Neck网络细节详解

3.2.5 Head输出端细节详解

4、YOLOv5网络代码实现

# 检测类
class Detect(nn.Module):
    stride = None  # strides computed during build
    export = False  # onnx export

    def __init__(self, nc=80, anchors=(), ch=()):  # detection layer
        super(Detect, self).__init__()
        self.nc = nc  # number of classes
        self.no = nc + 5  # number of outputs per anchor
        self.nl = len(anchors)  # number of detection layers
        self.na = len(anchors[0]) // 2  # number of anchors
        self.grid = [torch.zeros(1)] * self.nl  # init grid
        a = torch.tensor(anchors).float().view(self.nl, -1, 2)
        self.register_buffer('anchors', a)  # shape(nl,na,2)
        self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2))  # shape(nl,1,na,1,1,2)
        self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv

    def forward(self, x):
        # x = x.copy()  # for profiling
        z = []  # inference output
        self.training |= self.export
        for i in range(self.nl):
            x[i] = self.m[i](x[i])  # conv
            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
            x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()

            if not self.training:  # inference
                if self.grid[i].shape[2:4] != x[i].shape[2:4]:
                    self.grid[i] = self._make_grid(nx, ny).to(x[i].device)

                y = x[i].sigmoid()
                y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i]  # xy
                y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
                z.append(y.view(bs, -1, self.no))

        return x if self.training else (torch.cat(z, 1), x)

    @staticmethod
    def _make_grid(nx=20, ny=20):
        yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
        return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()

# 根据配置的.yaml文件搭建模型
class Model(nn.Module):
    def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None):  # model, input channels, number of classes
        super(Model, self).__init__()
        if isinstance(cfg, dict):
            self.yaml = cfg  # model dict
        else:  # is *.yaml
            import yaml  # for torch hub
            self.yaml_file = Path(cfg).name
            with open(cfg) as f:
                self.yaml = yaml.load(f, Loader=yaml.SafeLoader)  # model dict

        # Define model
        ch = self.yaml['ch'] = self.yaml.get('ch', ch)  # input channels
        if nc and nc != self.yaml['nc']:
            logger.info('Overriding model.yaml nc=%g with nc=%g' % (self.yaml['nc'], nc))
            self.yaml['nc'] = nc  # override yaml value
        self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelist
        self.names = [str(i) for i in range(self.yaml['nc'])]  # default names
        # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])

        # Build strides, anchors
        m = self.model[-1]  # Detect()
        if isinstance(m, Detect):
            s = 256  # 2x min stride
            m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))])  # forward
            m.anchors /= m.stride.view(-1, 1, 1)
            check_anchor_order(m)
            self.stride = m.stride
            self._initialize_biases()  # only run once
            # print('Strides: %s' % m.stride.tolist())

        # Init weights, biases
        initialize_weights(self)
        self.info()
        logger.info('')

    def forward(self, x, augment=False, profile=False):
        if augment:
            img_size = x.shape[-2:]  # height, width
            s = [1, 0.83, 0.67]  # scales
            f = [None, 3, None]  # flips (2-ud, 3-lr)
            y = []  # outputs
            for si, fi in zip(s, f):
                xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
                yi = self.forward_once(xi)[0]  # forward
                # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
                yi[..., :4] /= si  # de-scale
                if fi == 2:
                    yi[..., 1] = img_size[0] - yi[..., 1]  # de-flip ud
                elif fi == 3:
                    yi[..., 0] = img_size[1] - yi[..., 0]  # de-flip lr
                y.append(yi)
            return torch.cat(y, 1), None  # augmented inference, train
        else:
            return self.forward_once(x, profile)  # single-scale inference, train

    def forward_once(self, x, profile=False):
        y, dt = [], []  # outputs
        for m in self.model:
            if m.f != -1:  # if not from previous layer
                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers

            if profile:
                o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPS
                t = time_synchronized()
                for _ in range(10):
                    _ = m(x)
                dt.append((time_synchronized() - t) * 100)
                print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type))

            x = m(x)  # run
            y.append(x if m.i in self.save else None)  # save output

        if profile:
            print('%.1fms total' % sum(dt))
        return x

    def _initialize_biases(self, cf=None):  # initialize biases into Detect(), cf is class frequency
        # https://arxiv.org/abs/1708.02002 section 3.3
        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
        m = self.model[-1]  # Detect() module
        for mi, s in zip(m.m, m.stride):  # from
            b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
            b.data[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
            b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum())  # cls
            mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)

    def _print_biases(self):
        m = self.model[-1]  # Detect() module
        for mi in m.m:  # from
            b = mi.bias.detach().view(m.na, -1).T  # conv.bias(255) to (3,85)
            print(('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))

    # def _print_weights(self):
    #     for m in self.model.modules():
    #         if type(m) is Bottleneck:
    #             print('%10.3g' % (m.w.detach().sigmoid() * 2))  # shortcut weights

    def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
        print('Fusing layers... ')
        for m in self.model.modules():
            if type(m) is Conv and hasattr(m, 'bn'):
                m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
                delattr(m, 'bn')  # remove batchnorm
                m.forward = m.fuseforward  # update forward
        self.info()
        return self

    def nms(self, mode=True):  # add or remove NMS module
        present = type(self.model[-1]) is NMS  # last layer is NMS
        if mode and not present:
            print('Adding NMS... ')
            m = NMS()  # module
            m.f = -1  # from
            m.i = self.model[-1].i + 1  # index
            self.model.add_module(name='%s' % m.i, module=m)  # add
            self.eval()
        elif not mode and present:
            print('Removing NMS... ')
            self.model = self.model[:-1]  # remove
        return self

    def autoshape(self):  # add autoShape module
        print('Adding autoShape... ')
        m = autoShape(self)  # wrap model
        copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=())  # copy attributes
        return m

    def info(self, verbose=False, img_size=640):  # print model information
        model_info(self, verbose, img_size)

# 解析模型
def parse_model(d, ch):  # model_dict, input_channels(3)
    logger.info('\n%3s%18s%3s%10s  %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
    anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)

    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
    for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args
        m = eval(m) if isinstance(m, str) else m  # eval strings
        for j, a in enumerate(args):
            try:
                args[j] = eval(a) if isinstance(a, str) else a  # eval strings
            except:
                pass

        n = max(round(n * gd), 1) if n > 1 else n  # depth gain
        if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP,
                 C3]:
            c1, c2 = ch[f], args[0]

            # Normal
            # if i > 0 and args[0] != no:  # channel expansion factor
            #     ex = 1.75  # exponential (default 2.0)
            #     e = math.log(c2 / ch[1]) / math.log(2)
            #     c2 = int(ch[1] * ex ** e)
            # if m != Focus:

            c2 = make_pisible(c2 * gw, 8) if c2 != no else c2

            # Experimental
            # if i > 0 and args[0] != no:  # channel expansion factor
            #     ex = 1 + gw  # exponential (default 2.0)
            #     ch1 = 32  # ch[1]
            #     e = math.log(c2 / ch1) / math.log(2)  # level 1-n
            #     c2 = int(ch1 * ex ** e)
            # if m != Focus:
            #     c2 = make_pisible(c2, 8) if c2 != no else c2

            args = [c1, c2, *args[1:]]
            if m in [BottleneckCSP, C3]:
                args.insert(2, n)
                n = 1
        elif m is nn.BatchNorm2d:
            args = [ch[f]]
        elif m is Concat:
            c2 = sum([ch[x if x < 0 else x + 1] for x in f])
        elif m is Detect:
            args.append([ch[x + 1] for x in f])
            if isinstance(args[1], int):  # number of anchors
                args[1] = [list(range(args[1] * 2))] * len(f)
        elif m is Contract:
            c2 = ch[f if f < 0 else f + 1] * args[0] ** 2
        elif m is Expand:
            c2 = ch[f if f < 0 else f + 1] // args[0] ** 2
        else:
            c2 = ch[f if f < 0 else f + 1]

        m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args)  # module
        t = str(m)[8:-2].replace('__main__.', '')  # module type
        np = sum([x.numel() for x in m_.parameters()])  # number params
        m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
        logger.info('%3s%18s%3s%10.0f  %-40s%-30s' % (i, f, n, np, t, args))  # print
        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
        layers.append(m_)
        ch.append(c2)
    return nn.Sequential(*layers), sorted(save)

5、YOLOv5效果展示与分析

5.1、YOLOv5客观效果展示与分析


上图展示了不同版本的YOLOv5与EfficientDet检测算法之间的性能比较曲线图。横轴表示的是该算法在GPU上面的推理时间(ms/img)，该数值越小越好；纵轴表示的是该算法在COCO测试数据集上面的AP指标，该数值越大越好。通过观察我们可以得出以下的初步结论：（1）与EfficientDet0相比，YOLOv5s不仅可以获得更高的AP指标，而且可以获得更快的推理速度；（2）与EfficientDet4相比，YOLOv5x不仅能够获得更高的AP指标，其推理速度是它的1/5左右。

上表展示了不同版本的YOLOv5检测算法在COCO2017验证集与测试集上面的各项指标，具体包括：模型输入大小、AP50指标、Speed、FPS、params与GFLOPS。通过观察我们可以得出以下的初步结论：（1）YOLOv5s的输入图片分辨率为640*640，在COCO测试集与验证集上面的AP指标为36.8，AP50指标为55.6。该算法在V100 GPU上面的推理速度仅仅需要2.2ms，帧率为455FPS，该网络的模型大小仅为7.3M；（2）YOLOv5x的输入图像分辨率为640*640，在COCO测试集与验证集上面的AP指标为50.1，AP50指标为68.7。该算法在V100 GPU上面的推理速度仅仅需要6.0ms，帧率为167FPS，该网络的模型大小为87.7M。（3）我们可以根据现实场景的需要选择合适的模型，如果我们关注速度的话可以选择YOLOv5s模型；如果我们关注精度的话可以选择YOLOv5x模型。

5.2、YOLOv5主观效果展示与分析

yolov5 行人检测_车辆检测_电动车检测识别_交通标志识别

yolo5 车辆行人检测

6、总结与分析

YOLOv5是一种单阶段目标检测算法，该算法在YOLOv4的基础上添加了一些新的改进思路，使得其速度与精度都得到了极大的性能提升，具体包括：输入端的Mosaic数据增强、自适应锚框计算、自适应图片缩放操作；基准端的Focus结构与CSP结构；Neck端的SPP与FPN+PAN结构；输出端的损失函数GIOU_Loss以及预测框筛选的DIOU_nms。除此之外，YOLOv5中的各种改进思路仍然可以应用到其它的目标检测算法中。

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原文链接：https://blog.csdn.net/WZZ18191171661/article/details/113789486

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