Assisted labelling is inconsistent with the model results

Community Help
1

Assisted labelling is not returning the same results with the model results for the same pictures. Screenshots are below.

petpoo5___Annotate_and_petpoo5_-_v6_2024-01-06_1_12pm
petpoo5___Annotate_and_petpoo5_-_v6_2024-01-06_1_12pm1719×923 107 KB

Describe your question/issue here! (delete this when you post)

  • Project Type:Object detection
  • Operating System & Browser:Mac, Chrome, Both latest versions
  • Project Universe Link or Workspace/Project ID:Sign in to Roboflow
2

Based on the information provided in the extracts, there could be several reasons why the assisted labelling is not returning the same results as the model results for the same pictures.

  1. Model Confidence: The confidence level of the model can affect the results. You can adjust the confidence and overlap thresholds to adjust under what conditions a label will be applied. This can help reduce mislabeled objects. Source

  2. Model Compatibility: There could be compatibility issues with the model you’re using. For instance, in one of the forum posts, a user mentioned that Ultralyics had added operations in the YOLOv8 model architecture that were not compatible with non-async model execution. Source

  3. Data Quantity: If your model is not making predictions confidently, it could be due to insufficient data. Source

  4. Model Selection: Ensure that you have selected the correct model for Label Assist. You can select a model to which you have access through the Your Models tab. You can select any model from Universe through the Public Models tab. You need to star a model on Universe before it will show up in the Public Models tab. Source

If you’re still facing issues, it would be best to reach out to Roboflow’s support with the specific details of your problem.

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3

None of the bot responses are the reason. Need help from support.

4

same issue. in deploy page, the trained model can work very well, but the predictions of same model in label assist annotations is totally wrong.

5

err
err1088×1588 209 KB

this issue still exists.

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