superanimal_analyze_images(superanimal_name,
                           model_name,
                           detector_name= 'fasterrcnn_mobilenet_v3_large_fpn',
                           images = in_image_folder,
                           max_individuals = max_individuals,
                           out_folder = out_image_folder,
                           pose_threshold = 0.5,
                           bbox_threshold = 0.6,
                           plot_skeleton = True,
                           customized_model_config=customized_model_config,
                           customized_pose_checkpoint=customized_pose_checkpoint,
                           customized_detector_checkpoint=customized_detector_checkpoint)

Failed to load the snapshot: /content/drive/MyDrive/.../snapshot-detector-best-070.pt.
If you trust the snapshot that you're trying to load, you can try calling `Runner.load_snapshot` with `weights_only=False`. See the message below for more information and warnings.
You can set the `weights_only` parameter in the model configuration (the content of the pytorch_config.yaml), as:

runner:
  load_weights_only: False


---------------------------------------------------------------------------
UnpicklingError                           Traceback (most recent call last)
[<ipython-input-15-12fba7293495>](https://localhost:8080/#) in <cell line: 1>()
----> 1 superanimal_analyze_images(superanimal_name,
      2                            model_name,
      3                            detector_name= 'fasterrcnn_mobilenet_v3_large_fpn',
      4                            images = in_image_folder,
      5                            max_individuals = max_individuals,

9 frames
[/usr/local/lib/python3.10/dist-packages/deeplabcut/pose_estimation_pytorch/apis/analyze_images.py](https://localhost:8080/#) in superanimal_analyze_images(superanimal_name, model_name, detector_name, images, max_individuals, out_folder, progress_bar, device, pose_threshold, bbox_threshold, plot_skeleton, customized_model_config, customized_pose_checkpoint, customized_detector_checkpoint)
    181         config["detector"]["model"]["box_score_thresh"] = bbox_threshold
    182 
--> 183     predictions = analyze_image_folder(
    184         model_cfg=config,
    185         images=images,

[/usr/local/lib/python3.10/dist-packages/deeplabcut/pose_estimation_pytorch/apis/analyze_images.py](https://localhost:8080/#) in analyze_image_folder(model_cfg, images, snapshot_path, detector_path, frame_type, device, max_individuals, progress_bar)
    435         device = resolve_device(model_cfg)
    436 
--> 437     pose_runner, detector_runner = get_inference_runners(
    438         model_config=model_cfg,
    439         snapshot_path=snapshot_path,

[/usr/local/lib/python3.10/dist-packages/deeplabcut/pose_estimation_pytorch/apis/utils.py](https://localhost:8080/#) in get_inference_runners(model_config, snapshot_path, max_individuals, num_bodyparts, num_unique_bodyparts, batch_size, device, with_identity, transform, detector_batch_size, detector_path, detector_transform, dynamic)
    521                 detector_config["pretrained"] = False
    522 
--> 523             detector_runner = build_inference_runner(
    524                 task=Task.DETECT,
    525                 model=DETECTORS.build(detector_config),

[/usr/local/lib/python3.10/dist-packages/deeplabcut/pose_estimation_pytorch/runners/inference.py](https://localhost:8080/#) in build_inference_runner(task, model, device, snapshot_path, batch_size, preprocessor, postprocessor, dynamic, load_weights_only)
    370                 f"detection. Please turn off dynamic cropping."
    371             )
--> 372         return DetectorInferenceRunner(**kwargs)
    373 
    374     if task != Task.BOTTOM_UP:

[/usr/local/lib/python3.10/dist-packages/deeplabcut/pose_estimation_pytorch/runners/inference.py](https://localhost:8080/#) in __init__(self, model, **kwargs)
    291             **kwargs: Inference runner kwargs.
    292         """
--> 293         super().__init__(model, **kwargs)
    294 
    295     def predict(self, inputs: torch.Tensor) -> list[dict[str, dict[str, np.ndarray]]]:

[/usr/local/lib/python3.10/dist-packages/deeplabcut/pose_estimation_pytorch/runners/inference.py](https://localhost:8080/#) in __init__(self, model, batch_size, device, snapshot_path, preprocessor, postprocessor, load_weights_only)
     68 
     69         if self.snapshot_path is not None and self.snapshot_path != "":
---> 70             self.load_snapshot(
     71                 self.snapshot_path,
     72                 self.device,

[/usr/local/lib/python3.10/dist-packages/deeplabcut/pose_estimation_pytorch/runners/base.py](https://localhost:8080/#) in load_snapshot(snapshot_path, device, model, weights_only)
     84             The content of the snapshot file.
     85         """
---> 86         snapshot = attempt_snapshot_load(snapshot_path, device, weights_only)
     87         model.load_state_dict(snapshot["model"])
     88         return snapshot

[/usr/local/lib/python3.10/dist-packages/deeplabcut/pose_estimation_pytorch/runners/base.py](https://localhost:8080/#) in attempt_snapshot_load(path, device, weights_only)
    121             "  load_weights_only: False\n```\n"
    122         )
--> 123         raise err
    124 
    125     return snapshot

[/usr/local/lib/python3.10/dist-packages/deeplabcut/pose_estimation_pytorch/runners/base.py](https://localhost:8080/#) in attempt_snapshot_load(path, device, weights_only)
    109     """
    110     try:
--> 111         snapshot = torch.load(path, map_location=device, weights_only=weights_only)
    112     except pickle.UnpicklingError as err:
    113         print(

[/usr/local/lib/python3.10/dist-packages/torch/serialization.py](https://localhost:8080/#) in load(f, map_location, pickle_module, weights_only, mmap, **pickle_load_args)
   1357                         )
   1358                     except pickle.UnpicklingError as e:
-> 1359                         raise pickle.UnpicklingError(_get_wo_message(str(e))) from None
   1360                 return _load(
   1361                     opened_zipfile,

UnpicklingError: Weights only load failed. This file can still be loaded, to do so you have two options, do those steps only if you trust the source of the checkpoint. 
	(1) Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.
	(2) Alternatively, to load with `weights_only=True` please check the recommended steps in the following error message.
	WeightsUnpickler error: Unsupported global: GLOBAL numpy.core.multiarray.scalar was not an allowed global by default. Please use `torch.serialization.add_safe_globals([scalar])` to allowlist this global if you trust this class/function.

Check the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html.