opendatalab/MinerU · error · NotImplementedError

mode[{model_name}_model] is not implemented!

Error message

mode[{model_name}_model] is not implemented!

What it means

Raised by the MobileNetV3 detection backbone when model_name (the 'mode' config key) is not 'large' or 'small'. The backbone only contains layer tables for those two variants; any other mode string raises NotImplementedError at construction.

Source

Thrown at mineru/model/utils/pytorchocr/modeling/backbones/det_mobilenet_v3.py:199

            cls_ch_squeeze = 960
        elif model_name == "small":
            cfg = [
                # k, exp, c,  se,     nl,  s,
                [3, 16, 16, True, "relu", 2],
                [3, 72, 24, False, "relu", 2],
                [3, 88, 24, False, "relu", 1],
                [5, 96, 40, True, "hard_swish", 2],
                [5, 240, 40, True, "hard_swish", 1],
                [5, 240, 40, True, "hard_swish", 1],
                [5, 120, 48, True, "hard_swish", 1],
                [5, 144, 48, True, "hard_swish", 1],
                [5, 288, 96, True, "hard_swish", 2],
                [5, 576, 96, True, "hard_swish", 1],
                [5, 576, 96, True, "hard_swish", 1],
            ]
            cls_ch_squeeze = 576
        else:
            raise NotImplementedError(
                "mode[" + model_name + "_model] is not implemented!"
            )

        supported_scale = [0.35, 0.5, 0.75, 1.0, 1.25]
        assert (
            scale in supported_scale
        ), "supported scale are {} but input scale is {}".format(supported_scale, scale)
        inplanes = 16
        # conv1
        self.conv = ConvBNLayer(
            in_channels=in_channels,
            out_channels=make_divisible(inplanes * scale),
            kernel_size=3,
            stride=2,
            padding=1,
            groups=1,
            if_act=True,
            act="hard_swish",

View on GitHub (pinned to 4fe4bde114)

Solutions

1. Use model_name: 'large' or 'small' in the MobileNetV3 backbone config.
2. Pick a different supported backbone if you need another capacity point.
3. Check exact case: the comparison is case-sensitive.

Example fix

# before
Backbone:
  name: MobileNetV3
  model_name: medium
  scale: 0.5

# after
Backbone:
  name: MobileNetV3
  model_name: small
  scale: 0.5

model_name = backbone_cfg.get("model_name")
assert model_name in {"large", "small"}, f"MobileNetV3 det supports large/small, got {model_name!r}"

When it happens

Trigger: Building DetModel with backbone config {name: MobileNetV3, model_name: 'tiny'|'medium'|'xlarge'} or a misspelled value like 'Large'.

Common situations: Configs ported from torchvision MobileNetV3 (which has more width variants); case mismatches; configs edited to try unsupported scales.

Related errors

• mode[{model_name}_model] is not implemented!
• Unsupported activation: {name}
• PPLCNetV4 {mode} model_size must be one of {list(config_dict
• The mixer must be one of [Global, Local, Conv]
• {name} is not supported in MultiHead yet

AI-assisted analysis of opendatalab/MinerU@4fe4bde114 (2026-08-14). Data as JSON: /api/errors/fe0d48d7a4db0b2f. Report an issue: GitHub.