{"record":{"id":"20974586bbc8f750","repo":"tensorflow/models","slug":"the-minimum-backbone-level-d-should-be-less-or-eq","errorCode":null,"errorMessage":"The minimum backbone level %d should be less or equal to FPN minimum level %d.:","messagePattern":"The minimum backbone level (.+?) should be less or equal to FPN minimum level (.+?)\\.:","errorType":"exception","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"official/legacy/detection/modeling/architecture/fpn.py","lineNumber":119,"sourceCode":"          name='p%d' % level)\n\n  def __call__(self, multilevel_features, is_training=None):\n    \"\"\"Returns the FPN features for a given multilevel features.\n\n    Args:\n      multilevel_features: a `dict` containing `int` keys for continuous feature\n        levels, e.g., [2, 3, 4, 5]. The values are corresponding features with\n        shape [batch_size, height_l, width_l, num_filters].\n      is_training: `bool` if True, the model is in training mode.\n\n    Returns:\n      a `dict` containing `int` keys for continuous feature levels\n      [min_level, min_level + 1, ..., max_level]. The values are corresponding\n      FPN features with shape [batch_size, height_l, width_l, fpn_feat_dims].\n    \"\"\"\n    input_levels = list(multilevel_features.keys())\n    if min(input_levels) > self._min_level:\n      raise ValueError(\n          'The minimum backbone level %d should be '%(min(input_levels)) +\n          'less or equal to FPN minimum level %d.:'%(self._min_level))\n    backbone_max_level = min(max(input_levels), self._max_level)\n    with tf.name_scope('fpn'):\n      # Adds lateral connections.\n      feats_lateral = {}\n      for level in range(self._min_level, backbone_max_level + 1):\n        feats_lateral[level] = self._lateral_conv2d_op[level](\n            multilevel_features[level])\n\n      # Adds top-down path.\n      feats = {backbone_max_level: feats_lateral[backbone_max_level]}\n      for level in range(backbone_max_level - 1, self._min_level - 1, -1):\n        feats[level] = spatial_transform_ops.nearest_upsampling(\n            feats[level + 1], 2) + feats_lateral[level]\n\n      # Adds post-hoc 3x3 convolution kernel.\n      for level in range(self._min_level, backbone_max_level + 1):","sourceCodeStart":101,"sourceCodeEnd":137,"githubUrl":"https://github.com/tensorflow/models/blob/e006f5f0d534913e49c1f1dae87364039fa607e2/official/legacy/detection/modeling/architecture/fpn.py#L101-L137","documentation":"Error \"The minimum backbone level %d should be less or equal to FPN minimum level %d.:\" thrown in tensorflow/models.","triggerScenarios":"Thrown at official/legacy/detection/modeling/architecture/fpn.py:119 when the library encounters an invalid state.","commonSituations":"See trigger scenarios.","solutions":[],"exampleFix":null,"handlingStrategy":null,"validationCode":null,"typeGuard":null,"tryCatchPattern":null,"preventionTips":[],"tags":[],"backgroundTag":null,"analyzedSha":"e006f5f0d534913e49c1f1dae87364039fa607e2","analyzedAt":"2026-08-24T14:09:15.576Z","schemaVersion":2},"datasetVersion":"2026-08-24T17:17:21.512Z"}