yolov5m-cls.yaml

[yu1206]

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Question

yolov5m-cls.yaml is where

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yolov5m-cls.yaml is where

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[Y-T-G]

YOLOv5 classification models use the same YAML as detection models using the first 10 layers and then adding Classify head.

yolov5/models/yolo.py

Lines 361 to 365 in 567c664

	model.model = model.model[:cutoff] # backbone
	m = model.model[-1] # last layer
	ch = m.conv.in_channels if hasattr(m, "conv") else m.cv1.conv.in_channels # ch into module
	c = Classify(ch, nc) # Classify()
	c.i, c.f, c.type = m.i, m.f, "models.common.Classify" # index, from, type

[liuxuet]

YOLOv5 classification models use the same YAML as detection models using the first 10 layers and then adding Classify head.

yolov5/models/yolo.py

Lines 361 to 365 in 567c664

model.model = model.model[:cutoff] # backbone
m = model.model[-1] # last layer
ch = m.conv.in_channels if hasattr(m, "conv") else m.cv1.conv.in_channels # ch into module
c = Classify(ch, nc) # Classify()
c.i, c.f, c.type = m.i, m.f, "models.common.Classify" # index, from, type

So if I want to modify the model structure of the classification network, do I have to first modify the detection model, train it to obtain the model file, and then use this modified model file to pretrain the classification model?

[Y-T-G]

No. You just modify the classification model directly.

[liuxuet]

No. You just modify the classification model directly.

class ClassificationModel(BaseModel):
# YOLOv5 classification model
def init(self, cfg=None, model=None, nc=1000, cutoff=10): # yaml, model, number of classes, cutoff index
super().init()
self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg)

def _from_detection_model(self, model, nc=1000, cutoff=10):
    # Create a YOLOv5 classification model from a YOLOv5 detection model
    if isinstance(model, DetectMultiBackend):
        model = model.model  # unwrap DetectMultiBackend
    model.model = model.model[:cutoff]  # backbone
    m = model.model[-1]  # last layer
    ch = m.conv.in_channels if hasattr(m, 'conv') else m.cv1.conv.in_channels  # ch into module
    c = Classify(ch, nc)  # Classify()
    c.i, c.f, c.type = m.i, m.f, 'models.common.Classify'  # index, from, type
    model.model[-1] = c  # replace
    self.model = model.model
    self.stride = model.stride
    self.save = []
    self.nc = nc

def _from_yaml(self, cfg):
    # Create a YOLOv5 classification model from a *.yaml file
    self.model = None

But in the ClassificationModel class, the classification model is converted from the passed-in model, and its model structure is the same as that of the detection model.

[Y-T-G]

Yes, but you don't need to pretrain then detection model