classification .pt to onnx predict error about ultralytics HOT 5 OPEN

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Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

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from ultralytics.

@centurions hello! It looks like the issue you're encountering during prediction with the ONNX model might be related to the output tensor dimensions expected by the post-processing function. The error suggests that the output tensor shape does not match the expected shape, which is causing the assertion error.

A potential solution is to verify the output shapes of your ONNX model to ensure they match what the YOLOv8 framework expects for classification tasks. You can use tools like Netron to visualize the ONNX model and check the output dimensions.

If the dimensions are indeed different, you might need to adjust the export settings or modify the post-processing code to handle the shape returned by your ONNX model. Here's a quick way to check the output shape using ONNX:

import onnx
import onnxruntime as ort

# Load your model
onnx_model = onnx.load("best.onnx")
onnx.checker.check_model(onnx_model)

# Create a session and get the output
ort_session = ort.InferenceSession("best.onnx")
outputs = ort_session.run(None, {'input': your_input_tensor})
print('Output shape:', outputs[0].shape)

Adjust your_input_tensor to match the input size your model expects. This snippet will help you confirm if the output dimensions are as expected.

If you continue to experience issues, please provide more details about the output shapes, and we can explore further adjustments!

from ultralytics.

Hello Thank you
I have same error with openvino and others. I couldn't solve it. Do you have any other idea how it can be solved?

from ultralytics.

Hello @centurions,

Thank you for your patience. The issue seems to be consistent across multiple export formats, indicating it might be related to the output tensor dimensions expected by the post-processing function.

To troubleshoot further, I recommend checking the output shapes of your exported models. You can use tools like Netron to visualize the model and ensure the output dimensions match what YOLOv8 expects for classification tasks.

Additionally, you can inspect the output shapes using the following code snippet for ONNX:

import onnx
import onnxruntime as ort

# Load your model
onnx_model = onnx.load("best.onnx")
onnx.checker.check_model(onnx_model)

# Create a session and get the output
ort_session = ort.InferenceSession("best.onnx")
outputs = ort_session.run(None, {'input': your_input_tensor})
print('Output shape:', outputs[0].shape)

Replace your_input_tensor with the appropriate input tensor for your model. This will help confirm if the output dimensions are as expected.

If the issue persists, please share the output shapes, and we can explore further adjustments. Feel free to reach out if you need more assistance! 😊

from ultralytics.

@centurions perhaps you could try downgrading your torch install? I've seen other users having some issues with torch==2.3.0 and I was able to successful export and run inference using some of the standard assets.

from ultralytics.