George Pearse

As a warning I'm unlikely to give this a real crack until the 26th Dec 2021 and beyond. If anyone can give it a go before then by all means...

Hi @mfeurer sorry for the radio silence. You go for it, didn't really get anywhere. Looking forward to seeing the implementation though.

Hitting the same problem. Anyone had any luck with this?

Hit this and just needed to upgrade ``` pip install --upgrade onnx onnxruntime-gpu onnxruntime ```

Will be happy when no SSHing is required to deploy a model (just a streamlit UI or similar), even if it has to start the machine on AWS.

@RSKothari @nlp4whp It might even make a lot of sense to fork the embedding projector component and remove the in-browser interactive dimensionality reduction (to be replaced with whatever dimensionality reduction...

I'd really like to do what I can to keep this ticking over, @SkalskiP do you also think it's valuable? I'm always surprised by the lack of open-source implementations for...

Yeah all makes sense, tbh, the reason I want it to be integrated into supervision is to solve those very problems, at the minute I'm dealing with a lot of...

class id to optimal score, the minimum IoU to classify a prediction and annotation as a match is set upfront by the user. Is that not the far more common...

Complete pseudocode: ```python metrics = [] for class_name in class_list: for threshold in range(0, 1, 100): current_metric = calculate_metric(grid_of_matched_predictions_and_their_scores, metric='f1_score') metrics.append({ 'threshold': threshold, 'class_name': class_name, 'metric': metric, }) metrics_df =...