本文只介绍yolov5和yolov8两个版本的输出格式的区别；
yolo目标检测原理：使用yolo进行目标检测的主要思想是将目标检测任务转化为一个回归问题，通过前向传播过程完成目标的定位和分类；
YOLOv5输出格式
yolov5：每一anchar输出对应的xywh（4个通道)，框置信度（1个通道），类别置信度（类别数目，就是类别数个通道）；
这里的xy,wh的单位，不是像素， 是归一化后的数值， 需要按比例转换成像素值，xy的坐标是框框中心点的坐标，而不是左上角的坐标；
源代码在models\yolo.py 文件中 38 行可以体现；
def forward(self, x):
z = [] # inference output
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.dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)
if isinstance(self, Segment): # (boxes + masks)
xy, wh, conf, mask = x[i].split((2, 2, self.nc + 1, self.no - self.nc - 5), 4)
xy = (xy.sigmoid() * 2 + self.grid[i]) * self.stride[i] # xy
wh = (wh.sigmoid() * 2) ** 2 * self.anchor_grid[i] # wh
y = torch.cat((xy, wh, conf.sigmoid(), mask), 4)
else: # Detect (boxes only)
xy, wh, conf = x[i].sigmoid().split((2, 2, self.nc + 1), 4)
xy = (xy * 2 + self.grid[i]) * self.stride[i] # xy
wh = (wh * 2) ** 2 * self.anchor_grid[i] # wh
y = torch.cat((xy, wh, conf), 4)
z.append(y.view(bs, self.na * nx * ny, self.no))
return x if self.training else (torch.cat(z, 1), ) if self.export else (torch.cat(z, 1), x)YOLOv8输出格式
yolov8：每一anchar输出对应的xywh（4个通道)，类别置信度（类别数目，就是类别数个通道）；
这里的xy,wh的单位，不是像素， 是归一化后的数值， 需要按比例转换成像素值，xy的坐标是框框中心点的坐标，而不是左上角的坐标；
源代码在ultralytics/utils/ops.py 文件中 162行可以体现；
bs = prediction.shape[0] # batch size
nc = nc or (prediction.shape[1] - 4) # number of classes
nm = prediction.shape[1] - nc - 4
mi = 4 + nc # mask start index
xc = prediction[:, 4:mi].amax(1) > conf_thres # candidates
# Settings
# min_wh = 2 # (pixels) minimum box width and height
time_limit = 2.0 + max_time_img * bs # seconds to quit after
multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img)
prediction = prediction.transpose(-1, -2) # shape(1,84,6300) to shape(1,6300,84)
if not rotated:
if in_place:
prediction[..., :4] = xywh2xyxy(prediction[..., :4]) # xywh to xyxy
else:
prediction = torch.cat((xywh2xyxy(prediction[..., :4]), prediction[..., 4:]), dim=-1) # xywh to xyxy
t = time.time()
output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs
for xi, x in enumerate(prediction): # image index, image inference
# Apply constraints
# x[((x[:, 2:4] < min_wh) | (x[:, 2:4] > max_wh)).any(1), 4] = 0 # width-height
x = x[xc[xi]] # confidence
# Cat apriori labels if autolabelling
if labels and len(labels[xi]) and not rotated:
lb = labels[xi]
v = torch.zeros((len(lb), nc + nm + 4), device=x.device)
v[:, :4] = xywh2xyxy(lb[:, 1:5]) # box
v[range(len(lb)), lb[:, 0].long() + 4] = 1.0 # cls
x = torch.cat((x, v), 0)
# If none remain process next image
if not x.shape[0]:
continue
# Detections matrix nx6 (xyxy, conf, cls)
box, cls, mask = x.split((4, nc, nm), 1)
if multi_label:
i, j = torch.where(cls > conf_thres)
x = torch.cat((box[i], x[i, 4 + j, None], j[:, None].float(), mask[i]), 1)
else: # best class only
conf, j = cls.max(1, keepdim=True)
x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres]
# Filter by class
if classes is not None:
x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
# Check shape
n = x.shape[0] # number of boxes
if not n: # no boxes
continue
if n > max_nms: # excess boxes
x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence and remove excess boxes
# Batched NMS
c = x[:, 5:6] * (0 if agnostic else max_wh) # classes
scores = x[:, 4] # scores
if rotated:
boxes = torch.cat((x[:, :2] + c, x[:, 2:4], x[:, -1:]), dim=-1) # xywhr
i = nms_rotated(boxes, scores, iou_thres)
else:
boxes = x[:, :4] + c # boxes (offset by class)
i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS
i = i[:max_det] # limit detections
