We don’t have a tutorial to quantize a detection model, we can consider it for the future.  You can quantize the backbone only as follows, in pseudocode:
# original model (pseudocode)
class M(torch.nn.Module):
  def __init__(self, ...):
    ...
    self.backbone = ...
    self.rpn = ...
    self.head = ...

  def forward(self, x):
    features = self.backbone(x)
    proposals = self.rpn(features)
    head_results = self.head(features, proposals)
    return head_results

# modify M, place quants/dequants to prepare backbone for quantization
class MQuantizeable(torch.nn.Module):
  def __init__(self, ...):
    ...
    self.backbone = ...
    self.rpn = ...
    self.head = ...
  self.quant = torch.quantization.QuantStub()
  self.dequant = torch.quantization.DeQuantStub()

  def forward(self, x):
    # wrap backbone in quant/dequant
    x_quant = self.quant(x)
    features_quant = self.backbone(x)
    features = self.dequant(features_quant)
    proposals = self.rpn(features)
    head_results = self.head(features, proposals)
    return head_results

# quantization call
m = MQuantizeable(...)
# set qconfig on the backbone only
m.backbone.qconfig = ...
m = torch.quantization.prepare(m, ...)