{"record":{"id":"93788091839b187c","repo":"tensorflow/models","slug":"failed-to-load-state-dict-into-model-error","errorCode":null,"errorMessage":"Failed to load state dict into model: {error}","messagePattern":"Failed to load state dict into model: (.+?)","errorType":"exception","errorClass":"ClassifierError","httpStatus":null,"severity":"error","filePath":"official/projects/waste_identification_ml/model_inference_with_tracking/sam3_dinov3_tracking_pipeline/dinov3_classifier.py","lineNumber":351,"sourceCode":"    # DINOv3 Vision Transformers store token embedding dimension in\n    # norm.normalized_shape[0].\n    hidden_size = backbone_model.norm.normalized_shape[0]\n\n    pooling = _infer_pooling_from_state_dict(\n        saved_state_dict=saved_state_dict, hidden_size=hidden_size\n    )\n\n    model = Dinov3ClassificationModule(\n        backbone_model=backbone_model,\n        hidden_size=hidden_size,\n        number_of_classes=len(class_names),\n        pooling=pooling,\n    ).to(resolved_device)\n\n    try:\n      model.load_state_dict(saved_state_dict)\n    except RuntimeError as error:\n      raise ClassifierError(\n          f\"Failed to load state dict into model: {error}\"\n      ) from error\n\n    model.eval()\n    return cls(\n        model=model,\n        class_names=class_names,\n        image_transform=_build_image_transform(config),\n        device=resolved_device,\n    )\n\n  @torch.no_grad()\n  def predict_batch(self, images: Sequence[Image.Image]) -> list[Prediction]:\n    \"\"\"Classifies a batch of PIL images in a single forward pass.\n\n    Args:\n      images: Sequence of PIL images to classify.\n","sourceCodeStart":333,"sourceCodeEnd":369,"githubUrl":"https://github.com/tensorflow/models/blob/e006f5f0d534913e49c1f1dae87364039fa607e2/official/projects/waste_identification_ml/model_inference_with_tracking/sam3_dinov3_tracking_pipeline/dinov3_classifier.py#L333-L369","documentation":"Error \"Failed to load state dict into model: {error}\" thrown in tensorflow/models.","triggerScenarios":"Thrown at official/projects/waste_identification_ml/model_inference_with_tracking/sam3_dinov3_tracking_pipeline/dinov3_classifier.py:351 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"}