How to get the detection result from this model

[MarcoHuixxx]

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Question

The first pic is the model properties, there should be one float32 output. However, after executing model.runSync([data]). The output is the second pic. I have no idea about how to get the detection result from this model.

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[github-actions]

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[MarcoHuixxx]

anyone can help me!!!

[MarcoHuixxx]

please

[UltralyticsAssistant]

@MarcoHuixxx hello! It looks like you're trying to interpret the output from the model after running model.runSync([data]). The output you're seeing is likely a tensor or an array of values that represent detection results.

To extract meaningful detection information from this output, you typically need to post-process the data. This involves applying certain thresholds to filter out low-confidence detections, and then decoding the tensor to retrieve the bounding box coordinates, class IDs, and confidence scores for each detection.

The exact post-processing steps can vary depending on the model's output format. Generally, you would:

1. Apply a confidence threshold to discard detections with low confidence.
2. Apply non-maximum suppression (NMS) to remove overlapping bounding boxes.
3. Decode the remaining detections into a human-readable format (e.g., bounding box coordinates and class labels).

Please refer to the Ultralytics HUB Docs for guidance on post-processing the model's output. The documentation should provide you with the necessary steps and explanations on how to handle the output tensor to get the detection results you're looking for.

If you're still having trouble, feel free to provide more details about the output you're getting, and I'll do my best to assist you further. 😊

[MarcoHuixxx]

The output format of a sample tensorflowlite model from the Tensorflow official doc is
. There are 4 separate independent array and it will be easier to extract the results. But the output of the tensorflowlite model generated by ultralytics has only one array. May I know the differences between the coco model and model from ultralytics? Also, I cannot find any doc about post-processing the model's output in Ultralytics HUB Docs. Please attach it, thanks!

[UltralyticsAssistant]

@MarcoHuixxx the output format you're describing from the TensorFlow Lite model is indeed different from the Ultralytics model output. TensorFlow Lite models often provide separate arrays for boxes, classes, scores, and number of detections, which can be directly interpreted.

In contrast, Ultralytics models typically output a single tensor that contains all the detection information. This single tensor needs to be post-processed to separate out the bounding box coordinates, class IDs, and confidence scores.

The differences in output format are due to the way each model architecture is designed and how the outputs are structured. Ultralytics models are optimized for performance and may use a different output encoding to maximize speed and efficiency.

Regarding the documentation for post-processing the model's output, I apologize for any confusion. The Ultralytics HUB Docs should contain a section on how to interpret and handle the model outputs, including post-processing steps. If you're unable to find the relevant information, it's possible that the documentation may need to be updated to include these details.

As I'm unable to provide direct links or attachments here, I recommend checking the Ultralytics HUB Docs again for any updates or additional information that may have been added. If the information is still missing, please consider opening an issue on the GitHub repository to request detailed documentation on post-processing the outputs of Ultralytics models. The team values user feedback and strives to improve the documentation to better assist users like you. 😊

[github-actions]

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