Try to run yolov11 but errors ocuur. Need HELP!!!⚠️

[XYyang530]

I downloaded the ultralytics follow by the Ultralytics YOLO Docs and when I try to run the run"yolo predict model=yolo11n.pt imgsz=640 conf=0.25" as a example. But the anaconda prompt give me the errors:
(base) C:\Users\yxy25>yolo predict model=yolo11n.pt imgsz=640 conf=0.25
WARNING ⚠️ 'source' argument is missing. Using default 'source=C:\Users\yxy25\anaconda3\Lib\site-packages\ultralytics\assets'.
Ultralytics 8.3.32 🚀 Python-3.9.13 torch-2.5.1+cu118 CUDA:0 (NVIDIA GeForce RTX 3070 Ti Laptop GPU, 8192MiB)
YOLO11n summary (fused): 238 layers, 2,616,248 parameters, 0 gradients, 6.5 GFLOPs

Traceback (most recent call last):
File "C:\Users\yxy25\anaconda3\lib\runpy.py", line 197, in _run_module_as_main
return run_code(code, main_globals, None,
File "C:\Users\yxy25\anaconda3\lib\runpy.py", line 87, in run_code
exec(code, run_globals)
File "C:\Users\yxy25\anaconda3\Scripts\yolo.exe_main.py", line 7, in
File "C:\Users\yxy25\anaconda3\lib\site-packages\ultralytics\cfg_init.py", line 969, in entrypoint
getattr(model, mode)(**overrides) # default args from model
File "C:\Users\yxy25\anaconda3\lib\site-packages\ultralytics\engine\model.py", line 554, in predict
return self.predictor.predict_cli(source=source) if is_cli else self.predictor(source=source, stream=stream)
File "C:\Users\yxy25\anaconda3\lib\site-packages\ultralytics\engine\predictor.py", line 184, in predict_cli
for _ in gen: # sourcery skip: remove-empty-nested-block, noqa
File "C:\Users\yxy25\anaconda3\lib\site-packages\torch\utils_contextlib.py", line 36, in generator_context
response = gen.send(None)
File "C:\Users\yxy25\anaconda3\lib\site-packages\ultralytics\engine\predictor.py", line 262, in stream_inference
self.results = self.postprocess(preds, im, im0s)
File "C:\Users\yxy25\anaconda3\lib\site-packages\ultralytics\models\yolo\detect\predict.py", line 25, in postprocess
preds = ops.non_max_suppression(
File "C:\Users\yxy25\anaconda3\lib\site-packages\ultralytics\utils\ops.py", line 292, in non_max_suppression
i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS
File "C:\Users\yxy25\anaconda3\lib\site-packages\torchvision\ops\boxes.py", line 41, in nms
return torch.ops.torchvision.nms(boxes, scores, iou_threshold)
File "C:\Users\yxy25\anaconda3\lib\site-packages\torch_ops.py", line 1116, in call
return self._op(*args, **(kwargs or {}))
NotImplementedError: Could not run 'torchvision::nms' with arguments from the 'CUDA' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'torchvision::nms' is only available for these backends: [CPU, Meta, QuantizedCPU, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradMPS, AutogradXPU, AutogradHPU, AutogradLazy, AutogradMeta, Tracer, AutocastCPU, AutocastXPU, AutocastMPS, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].

CPU: registered at C:\actions-runner_work\vision\vision\pytorch\vision\torchvision\csrc\ops\cpu\nms_kernel.cpp:112 [kernel]
Meta: registered at /dev/null:184 [kernel]
QuantizedCPU: registered at C:\actions-runner_work\vision\vision\pytorch\vision\torchvision\csrc\ops\quantized\cpu\qnms_kernel.cpp:124 [kernel]
BackendSelect: fallthrough registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\core\BackendSelectFallbackKernel.cpp:3 [backend fallback]
Python: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\core\PythonFallbackKernel.cpp:153 [backend fallback]
FuncTorchDynamicLayerBackMode: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\functorch\DynamicLayer.cpp:497 [backend fallback]
Functionalize: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\FunctionalizeFallbackKernel.cpp:349 [backend fallback]
Named: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\core\NamedRegistrations.cpp:7 [backend fallback]
Conjugate: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\ConjugateFallback.cpp:17 [backend fallback]
Negative: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\NegateFallback.cpp:18 [backend fallback]
ZeroTensor: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\ZeroTensorFallback.cpp:86 [backend fallback]
ADInplaceOrView: fallthrough registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\core\VariableFallbackKernel.cpp:96 [backend fallback]
AutogradOther: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\core\VariableFallbackKernel.cpp:63 [backend fallback]
AutogradCPU: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\core\VariableFallbackKernel.cpp:67 [backend fallback]
AutogradCUDA: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\core\VariableFallbackKernel.cpp:75 [backend fallback]
AutogradXLA: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\core\VariableFallbackKernel.cpp:79 [backend fallback]
AutogradMPS: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\core\VariableFallbackKernel.cpp:87 [backend fallback]
AutogradXPU: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\core\VariableFallbackKernel.cpp:71 [backend fallback]
AutogradHPU: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\core\VariableFallbackKernel.cpp:100 [backend fallback]
AutogradLazy: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\core\VariableFallbackKernel.cpp:83 [backend fallback]
AutogradMeta: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\core\VariableFallbackKernel.cpp:91 [backend fallback]
Tracer: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\torch\csrc\autograd\TraceTypeManual.cpp:294 [backend fallback]
AutocastCPU: registered at C:\actions-runner_work\vision\vision\pytorch\vision\torchvision\csrc\ops\autocast\nms_kernel.cpp:34 [kernel]
AutocastXPU: registered at C:\actions-runner_work\vision\vision\pytorch\vision\torchvision\csrc\ops\autocast\nms_kernel.cpp:41 [kernel]
AutocastMPS: fallthrough registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\autocast_mode.cpp:209 [backend fallback]
AutocastCUDA: registered at C:\actions-runner_work\vision\vision\pytorch\vision\torchvision\csrc\ops\autocast\nms_kernel.cpp:27 [kernel]
FuncTorchBatched: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\functorch\LegacyBatchingRegistrations.cpp:731 [backend fallback]
BatchedNestedTensor: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\functorch\LegacyBatchingRegistrations.cpp:758 [backend fallback]
FuncTorchVmapMode: fallthrough registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\functorch\VmapModeRegistrations.cpp:27 [backend fallback]
Batched: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\LegacyBatchingRegistrations.cpp:1075 [backend fallback]
VmapMode: fallthrough registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\VmapModeRegistrations.cpp:33 [backend fallback]
FuncTorchGradWrapper: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\functorch\TensorWrapper.cpp:207 [backend fallback]
PythonTLSSnapshot: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\core\PythonFallbackKernel.cpp:161 [backend fallback]
FuncTorchDynamicLayerFrontMode: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\functorch\DynamicLayer.cpp:493 [backend fallback]
PreDispatch: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\core\PythonFallbackKernel.cpp:165 [backend fallback]
PythonDispatcher: registered at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\core\PythonFallbackKernel.cpp:157 [backend fallback]

[UltralyticsAssistant]

👋 Hello @XYyang530, thanks for reaching out about your experience with Ultralytics 🚀! We're here to help you get things running smoothly. Make sure to check out our comprehensive Docs for guidance on getting started, including examples on using Python and CLI.

It seems like you might be encountering a 🐛 bug. Could you please provide a minimum reproducible example with detailed steps so that we can better assist you? This will help us understand the issue thoroughly.

Additionally, ensure that your Python environment is up-to-date and compatible with all dependencies. You can upgrade to the latest ultralytics package and its requirements using the following command in a Python>=3.8 environment with PyTorch>=1.8:

pip install -U ultralytics

Join our lively community for more interactive help. On Discord 🎧, you can chat in real-time, or for more detailed discussions visit Discourse. Also, explore our Subreddit to learn and share experiences with others.

Our environments for YOLO are constantly updated to ensure the best compatibility. You can run YOLO using:

• Notebooks with GPU access:
• Google Cloud & AWS: Quickstart guides available.
• Docker: Follow our Docker Quickstart Guide

This response is automated, but rest assured, a member of our Ultralytics engineering team will review your issue soon!

[glenn-jocher]

@XYyang530 based on the error message, it appears you're encountering a CUDA compatibility issue with torchvision's NMS operation. Try reinstalling PyTorch and torchvision with CUDA support using:

pip uninstall torch torchvision
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

If the issue persists, you can temporarily force CPU inference by adding device=cpu to your command:

yolo predict model=yolo11n.pt imgsz=640 conf=0.25 device=cpu

For more details on resolving CUDA-related issues, see our troubleshooting guide at https://docs.ultralytics.com/guides/yolo-common-issues/.