@Bobo-y

[farzadimanpour]

@Bobo-y
Hello,
Can you explain this answer more?
Which parts should I modify? And what is your suggestion for possible modifications to have better results in the case of small object detection with this baseline?
Thanks

Originally posted by @farzadips in #97 (comment)

[Bobo~]

@farzadimanpour OK，I'll give you a detailed answer at the weekend. I'm busy with my work. sorry

[Bobo~]

Originally posted by @farzadips in #97 (comment)

for backbone, such as YOLO backbone, you want to detect more small object. you need output C2

'
def forward(self, x):

    c1 = self.C1(x)

    c2 = self.C2(c1)
    conv1 = self.conv1(c2)

    c3 = self.C3(conv1)
    conv2 = self.conv2(c3)

    c4 = self.C4(conv2)
    conv3 = self.conv3(c4)

    c5 = self.C5(conv3)
    conv4 = self.conv4(c5)

    sppf = self.sppf(conv4)

    return c2, c3, c4, sppf

'

for FPN

`

def forward(self, inputs):

   C2, C3, C4, C5 = inputs

    P5 = self.P5(C5) 
    up5 = self.P5_upsampled(P5)
    concat1 = self.concat([up5, C4])
    conv1 = self.conv1(concat1)

    P4 = self.P4(conv1)
    up4 = self.P4_upsampled(P4)
    concat2 = self.concat([C3, up4])
    
    PP3 = self.P3(concat2)
    
    PP2 = ......

    you should add some op in here,  inclue conv PP3 then upsample and concat, so you will get  PP2

    return PP2, PP3, P4, P5

`

for PAN ,just like FPN,

`
def forward(self, inputs):

    PP2,  PP3, P4, P5 = inputs
    
   PP3 = ......
   you should add some op in here, just like PP4, PP5

    convp3 = self.convP3(PP3)
    concat3_4 = self.concat([convp3, P4])
    PP4 = self.P4(concat3_4)

    convp4 = self.convP4(PP4)
    concat4_5 = self.concat([convp4, P5])
    PP5 = self.P5(concat4_5)

    return PP2, PP3, PP4, PP5

`

for class YOLOHead

`
class YOLOHead(nn.Module):

stride = None
export = False
onnx_dynamic = False
def __init__(self, nc, anchors=None, ch=(256, 512, 1024), stride=[8., 16., 32.], inplace=True):  # detection layer
    super(YOLOHead, self).__init__()
    if anchors is None:
        anchors = [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]]
    else:
        anchors = anchors

`
you should change ch as a tuple with four elements, stride as (4., 8., 16., 32.)

for configs/model_yolo.yaml

rewrite as
`

stride: [4.0, 8.0, 16.0, 32.0]

anchors:
- [9,11, 21,19, 17,41] # this init value may not right
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32

`